diff --git a/aframe/pipelines/sandbox/configs/base.cfg b/aframe/pipelines/sandbox/configs/base.cfg
index e7cd32ba7..79f15d130 100644
--- a/aframe/pipelines/sandbox/configs/base.cfg
+++ b/aframe/pipelines/sandbox/configs/base.cfg
@@ -23,11 +23,11 @@ seed = 1122
 streams_per_gpu = 6
 
 # waveform parameters
-waveform_approximant = IMRPhenomXPHM
+waveform_approximant = IMRPhenomPv2
 waveform_duration = 8
 minimum_frequency = 20
 reference_frequency = 50
-coalescence_time = 6
+right_pad = 2
 
 # training parameters
 kernel_length = 1.5
@@ -70,7 +70,7 @@ waveform_duration = &::luigi_base::waveform_duration
 minimum_frequency = &::luigi_base::minimum_frequency
 reference_frequency = &::luigi_base::reference_frequency
 waveform_approximant = &::luigi_base::waveform_approximant
-coalescence_time = &::luigi_base::coalescence_time
+right_pad = &::luigi_base::right_pad
 prior = &::luigi_base::prior
 request_memory = 64GB
 
@@ -86,7 +86,7 @@ waveform_duration = &::luigi_base::waveform_duration
 minimum_frequency = &::luigi_base::minimum_frequency
 reference_frequency = &::luigi_base::reference_frequency
 waveform_approximant = &::luigi_base::waveform_approximant
-coalescence_time = &::luigi_base::coalescence_time
+right_pad = &::luigi_base::right_pad
 prior = &::luigi_base::prior
 snr_threshold = 4
 request_memory = 16GB
@@ -124,7 +124,7 @@ minimum_frequency = &::luigi_base::minimum_frequency
 reference_frequency = &::luigi_base::reference_frequency
 waveform_duration = &::luigi_base::waveform_duration
 waveform_approximant = &::luigi_base::waveform_approximant
-coalescence_time = &::luigi_base::coalescence_time
+right_pad = &::luigi_base::right_pad
 highpass = &::luigi_base::highpass
 lowpass = &::luigi_base::lowpass
 request_memory = 16GB
diff --git a/aframe/pipelines/sandbox/configs/bbh.cfg b/aframe/pipelines/sandbox/configs/bbh.cfg
index 768db8aa2..a1aa04d59 100644
--- a/aframe/pipelines/sandbox/configs/bbh.cfg
+++ b/aframe/pipelines/sandbox/configs/bbh.cfg
@@ -11,7 +11,7 @@ inherit = $AFRAME_REPO/aframe/pipelines/sandbox/configs/base.cfg
 # override bbh specific parameters
 [luigi_base]
 waveform_duration = 8
-coalescence_time = 4
+right_pad = 2
 kernel_length = 1.5
 prior = priors.priors.end_o3_ratesandpops
 
@@ -29,3 +29,4 @@ lowpass = &::luigi_base::lowpass
 fduration = &::luigi_base::fduration
 seed = &::luigi_base::seed
 use_wandb = true
+precompute_train_waveforms = true
diff --git a/aframe/pipelines/sandbox/configs/bns.cfg b/aframe/pipelines/sandbox/configs/bns.cfg
index 82c705e9a..867f8c775 100644
--- a/aframe/pipelines/sandbox/configs/bns.cfg
+++ b/aframe/pipelines/sandbox/configs/bns.cfg
@@ -13,7 +13,7 @@ inherit = $AFRAME_REPO/aframe/pipelines/sandbox/configs/base.cfg
 # override bns specific parameters
 [luigi_base]
 waveform_duration = 90
-coalescence_time = 45
+right_pad = 45
 kernel_length = 8
 prior = priors.priors.end_o3_ratesandpops_bns
 q = 45.6 
@@ -40,3 +40,4 @@ seed = &::luigi_base::seed
 fftlength = &::luigi_base::fftlength
 q = &::luigi_base::q
 use_wandb = true
+precompute_train_waveforms = true
diff --git a/aframe/pipelines/sandbox/configs/review.cfg b/aframe/pipelines/sandbox/configs/review.cfg
index b2f167d6f..1c678ee30 100644
--- a/aframe/pipelines/sandbox/configs/review.cfg
+++ b/aframe/pipelines/sandbox/configs/review.cfg
@@ -27,7 +27,7 @@ waveform_approximant = IMRPhenomPv2
 waveform_duration = 8
 minimum_frequency = 20
 reference_frequency = 50
-coalescence_time = 6
+right_pad = 2
 
 # training parameters
 kernel_length = 1.5
@@ -79,7 +79,7 @@ minimum_frequency = &::luigi_base::minimum_frequency
 reference_frequency = &::luigi_base::reference_frequency
 waveform_duration = &::luigi_base::waveform_duration
 waveform_approximant = &::luigi_base::waveform_approximant
-coalescence_time = &::luigi_base::coalescence_time
+right_pad = &::luigi_base::right_pad
 highpass = &::luigi_base::highpass
 lowpass = &::luigi_base::lowpass
 request_memory = 6GB
diff --git a/aframe/tasks/data/waveforms/base.py b/aframe/tasks/data/waveforms/base.py
index 7df9aaa7f..c9584dc9b 100644
--- a/aframe/tasks/data/waveforms/base.py
+++ b/aframe/tasks/data/waveforms/base.py
@@ -29,9 +29,10 @@ class WaveformParams(law.Task):
         default="IMRPhenomXPHM",
         description="Approximant to use for waveform generation",
     )
-    coalescence_time = luigi.FloatParameter(
+    right_pad = luigi.FloatParameter(
         description="Location of the defining point of the signal "
-        "within the generated waveform"
+        "within the generated waveform relative to the right edge "
+        "of the waveform (in seconds)",
     )
 
 
diff --git a/aframe/tasks/data/waveforms/testing.py b/aframe/tasks/data/waveforms/testing.py
index f77c21f3d..319753084 100644
--- a/aframe/tasks/data/waveforms/testing.py
+++ b/aframe/tasks/data/waveforms/testing.py
@@ -182,7 +182,7 @@ def run(self):
                 self.sample_rate,
                 self.waveform_duration,
                 self.waveform_approximant,
-                self.coalescence_time,
+                self.right_pad,
                 self.highpass,
                 self.lowpass,
                 self.snr_threshold,
diff --git a/aframe/tasks/data/waveforms/training.py b/aframe/tasks/data/waveforms/training.py
index 092500df4..c04d49a88 100644
--- a/aframe/tasks/data/waveforms/training.py
+++ b/aframe/tasks/data/waveforms/training.py
@@ -1,3 +1,6 @@
+from pathlib import Path
+import shutil
+
 import law
 from luigi.util import inherits
 
@@ -10,7 +13,7 @@
 
 
 @inherits(WaveformParams)
-class TrainingWaveforms(
+class DeployTrainingWaveforms(
     AframeDataTask, DeployTask, law.LocalWorkflow, StaticMemoryWorkflow
 ):
     """
@@ -24,14 +27,20 @@ class TrainingWaveforms(
 
     output_dir = PathParameter(
         description="Directory where merged training waveforms will be saved",
-        default=paths().train_datadir / "training_waveforms",
+        default=paths().train_datadir,
+    )
+
+    tmp_dir = PathParameter(
+        description="Directory where temporary "
+        "waveforms will be saved before being merged",
+        default=paths().tmp_dir / "training_waveforms",
     )
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
     def output(self):
-        return s3_or_local(self.output_dir / f"waveforms-{self.branch}.hdf5")
+        return s3_or_local(self.tmp_dir / f"waveforms-{self.branch}.hdf5")
 
     def run(self):
         from data.waveforms.training import training_waveforms
@@ -47,8 +56,57 @@ def run(self):
             minimum_frequency=self.minimum_frequency,
             reference_frequency=self.reference_frequency,
             waveform_approximant=self.waveform_approximant,
-            coalescence_time=self.coalescence_time,
+            right_pad=self.right_pad,
         )
         chunks = (min(64, num_signals), waveforms.get_waveforms().shape[-1])
         with self.output().open("w") as f:
             waveforms.write(f, chunks=chunks)
+
+
+@inherits(DeployTrainingWaveforms)
+class TrainingWaveforms(AframeDataTask):
+    """
+    Launch condorized generation of training waveforms,
+    and merge results into a single file
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.output_file = self.output_dir / "training_waveforms.hdf5"
+
+    def output(self):
+        return s3_or_local(self.output_file)
+
+    def requires(self):
+        return DeployTrainingWaveforms.req(
+            self,
+            workflow=self.workflow,
+            request_memory=self.request_memory,
+            request_disk=self.request_disk,
+            request_cpus=self.request_cpus,
+        )
+
+    @property
+    def targets(self):
+        return list(self.input().collection.targets.values())
+
+    @property
+    def waveform_files(self):
+        return list(map(Path, [targets.path for targets in self.targets]))
+
+    def run(self):
+        from ledger.injections import (
+            WaveformPolarizationSet,
+            waveform_class_factory,
+        )
+
+        cls = waveform_class_factory(
+            ["cross", "plus"],
+            WaveformPolarizationSet,
+            "WaveformPolarizationSet",
+        )
+        with self.output().open("w") as f:
+            cls.aggregate(self.waveform_files, f, clean=True)
+
+        # clean up temporary directory
+        shutil.rmtree(self.tmp_dir)
diff --git a/aframe/tasks/data/waveforms/validation.py b/aframe/tasks/data/waveforms/validation.py
index 7de8ea9b5..8c1491fcf 100644
--- a/aframe/tasks/data/waveforms/validation.py
+++ b/aframe/tasks/data/waveforms/validation.py
@@ -124,7 +124,7 @@ def run(self):
             self.sample_rate,
             self.waveform_duration,
             self.waveform_approximant,
-            self.coalescence_time,
+            self.right_pad,
             self.highpass,
             self.lowpass,
             self.snr_threshold,
diff --git a/aframe/tasks/train/base.py b/aframe/tasks/train/base.py
index 49a7c3fea..46f01a2b7 100644
--- a/aframe/tasks/train/base.py
+++ b/aframe/tasks/train/base.py
@@ -57,11 +57,20 @@ class TrainBaseParameters(law.Task):
     )
     data_dir = PathParameter(
         description="Directory where training data is stored."
-        "It is expected to contain a `signals.hdf5` file of signals, "
-        "and a `/background` sub-directory containing background "
-        "files used for training",
+        "It is expected to contain a `val_waveforms.hdf5` file of "
+        "signals for validation, a `/background` sub-directory containing "
+        "background, and a `training_waveforms.hdf5` file containing "
+        "training signals if `precompute_train_waveforms` is set to True.",
         default=paths().train_datadir,
     )
+    precompute_train_waveforms = luigi.BoolParameter(
+        default=False,
+        description="Whether to pre-compute the waveforms used "
+        "during training. If True, the training waveforms will be "
+        "read from the `training_waveforms.hdf5` file in the data "
+        "directory. If False, the waveforms will be simulated "
+        "on-the-fly during training.",
+    )
 
 
 @inherits(TrainBaseParameters)
@@ -69,9 +78,9 @@ class TrainBase(law.Task):
     def requires(self):
         reqs = {}
         reqs["strain"] = FetchTrain.req(self)
-        reqs["train_waveforms"] = TrainingWaveforms.req(self)
-
         reqs["val_waveforms"] = ValidationWaveforms.req(self)
+        if self.precompute_train_waveforms:
+            reqs["train_waveforms"] = TrainingWaveforms.req(self)
         return reqs
 
     def configure_wandb(self, args: List[str]) -> None:
diff --git a/docs/projects/data.md b/docs/projects/data.md
index 5dbd6febb..bacc1968a 100644
--- a/docs/projects/data.md
+++ b/docs/projects/data.md
@@ -76,7 +76,7 @@ apptainer run $AFRAME_CONTAINER_ROOT/data.sif \
     --minimum_frequency 20 \
     --reference_frequency 50 \
     --waveform_approximant IMRPhenomXPHM \
-    --coalescence_time 6 \
+    --right_pad 2 \
     --output_file ~/aframe/data/train/train_waveforms.hdf5
 ```
 
@@ -93,7 +93,7 @@ apptainer run $AFRAME_CONTAINER_ROOT/data.sif \
     --sample_rate 2048 \
     --waveform_duration 8 \
     --waveform_approximant IMRPhenomXPHM \
-    --coalescence_time 6 \
+    --right_pad 2 \
     --highpass 32 \
     --snr_threshold 4 \
     --psd ~/aframe/data/train/background/background-1240579783-7829.hdf5
diff --git a/libs/ledger/ledger/injections.py b/libs/ledger/ledger/injections.py
index d111ca25e..c45c30892 100644
--- a/libs/ledger/ledger/injections.py
+++ b/libs/ledger/ledger/injections.py
@@ -234,7 +234,7 @@ def convert_to_bilby_param_set(self, reference_frequency: float):
 class InjectionMetadata(Ledger):
     sample_rate: np.ndarray = metadata()
     duration: np.ndarray = metadata()
-    coalescence_time: float = metadata()
+    right_pad: float = metadata()
     num_injections: int = metadata(default=0)
 
     def __post_init__(self):
@@ -292,15 +292,15 @@ class _WaveformGenerator:
     waveform_approximant: str
     sample_rate: float
     waveform_duration: float
-    coalescence_time: float
+    right_pad: float
     minimum_frequency: float
     reference_frequency: float
 
     def shift_coalescence(self, waveforms: np.ndarray, t_final: float):
         """
         Shift a pair of polarizations such that the coalescence point is moved
-        to the time specified by self.coalescence_time. The shift is
-        accomplished by rolling the array
+        such that it is `self.right_pad` seconds from the right edge.
+        The shift is accomplished by rolling the array
 
         Args:
             waveforms:
@@ -314,11 +314,7 @@ def shift_coalescence(self, waveforms: np.ndarray, t_final: float):
         Returns:
             The stacked, shifted polarizations
         """
-        shift_time = (
-            t_final
-            - (self.waveform_duration - self.coalescence_time)
-            + 1 / self.sample_rate
-        )
+        shift_time = t_final - self.right_pad + 1 / self.sample_rate
         shift_idx = int(shift_time * self.sample_rate)
         return np.roll(waveforms, shift_idx, axis=-1)
 
@@ -403,20 +399,20 @@ def from_parameters(
         sample_rate: float,
         waveform_duration: float,
         waveform_approximant: str,
-        coalescence_time: float,
+        right_pad: float,
         ex: Optional[Executor] = None,
     ):
-        if waveform_duration < coalescence_time:
+        if waveform_duration < right_pad:
             raise ValueError(
-                "Coalescence time must be less than waveform duration; "
-                f"got values of {coalescence_time} and {waveform_duration}"
+                "Right padding must be less than waveform duration; "
+                f"got values of {right_pad} and {waveform_duration}"
             )
 
         waveform_generator = _WaveformGenerator(
             waveform_approximant=waveform_approximant,
             sample_rate=sample_rate,
             waveform_duration=waveform_duration,
-            coalescence_time=coalescence_time,
+            right_pad=right_pad,
             minimum_frequency=minimum_frequency,
             reference_frequency=reference_frequency,
         )
@@ -448,7 +444,7 @@ def from_parameters(
         polarizations["sample_rate"] = sample_rate
         polarizations["duration"] = waveform_duration
         polarizations["num_injections"] = len(params)
-        polarizations["coalescence_time"] = coalescence_time
+        polarizations["right_pad"] = right_pad
         return cls(**polarizations)
 
 
@@ -491,8 +487,14 @@ def waveforms(self) -> np.ndarray:
         if self._waveforms is None:
             fields = sorted(self.waveform_fields)
             waveforms = [getattr(self, i) for i in fields]
-            waveforms = np.stack(waveforms, axis=1)
-            self._waveforms = waveforms
+            shape = (
+                waveforms[0].shape[0],
+                len(fields),
+                waveforms[0].shape[-1],
+            )
+            self._waveforms = np.zeros(shape, dtype=np.float32)
+            for i, field in enumerate(fields):
+                self._waveforms[:, i, :] = getattr(self, field)
         return self._waveforms
 
     def num_waveform_fields(self):
@@ -560,14 +562,14 @@ def read(
             if all(i is None for i in [start, end, shifts]):
                 return cls._load_with_idx(f, None)
 
-            coalescence_time = f.attrs["coalescence_time"]
+            left_pad = f.attrs["duration"] - f.attrs["right_pad"]
             times = f["parameters"]["injection_time"][:]
 
             mask = True
             if start is not None:
-                mask &= (times + coalescence_time) >= start
+                mask &= (times + left_pad) >= start
             if end is not None:
-                mask &= (times - coalescence_time) <= end
+                mask &= (times - left_pad) <= end
             if shifts is not None:
                 shifts = np.array(shifts)
                 ndim = shifts.ndim
@@ -612,10 +614,10 @@ def inject(self, x: np.ndarray, start: float):
         initial timestamp `start`
         """
         stop = start + x.shape[-1] / self.sample_rate
-        post_coalescence_time = self.duration - self.coalescence_time
+        left_pad = self.duration - self.right_pad
 
-        mask = self.injection_time >= (start - self.coalescence_time)
-        mask &= self.injection_time <= (stop + post_coalescence_time)
+        mask = self.injection_time >= (start - left_pad)
+        mask &= self.injection_time <= (stop + self.right_pad)
 
         if not mask.any():
             return x
@@ -626,7 +628,7 @@ def inject(self, x: np.ndarray, start: float):
         # potentially pad x to inject waveforms
         # that fall over the boundaries of chunks
         pad = []
-        earliest = (times - self.coalescence_time - start).min()
+        earliest = (times - left_pad - start).min()
         if earliest < 0:
             # For consistency, we want to round down here
             # E.g., if earliest = -0.1 and sample_rate = 2048,
@@ -639,7 +641,7 @@ def inject(self, x: np.ndarray, start: float):
         else:
             pad.append(0)
 
-        latest = (times + post_coalescence_time - stop).max()
+        latest = (times + self.right_pad - stop).max()
         if latest > 0:
             num_late = int(latest * self.sample_rate)
             pad.append(num_late)
@@ -653,9 +655,8 @@ def inject(self, x: np.ndarray, start: float):
         # create matrix of indices of waveform_size for each waveform
         waveforms = waveforms.transpose((1, 0, 2))
         _, num_waveforms, waveform_size = waveforms.shape
-        coalescence_time_idx = int(self.coalescence_time * self.sample_rate)
 
-        idx = np.arange(waveform_size) - coalescence_time_idx
+        idx = np.arange(waveform_size) - int(left_pad * self.sample_rate)
         idx = idx[None]
         idx = np.repeat(idx, num_waveforms, axis=0)
 
diff --git a/libs/ledger/tests/test_injections.py b/libs/ledger/tests/test_injections.py
index 3c9fef40c..53f5ce3c3 100644
--- a/libs/ledger/tests/test_injections.py
+++ b/libs/ledger/tests/test_injections.py
@@ -115,8 +115,8 @@ def waveform_duration(self):
     def sample_rate(self):
         return 2048
 
-    @pytest.fixture(params=[0, 4, 7, 8])
-    def coalescence_time(self, request):
+    @pytest.fixture(params=[0, 0.5, 1, 4])
+    def right_pad(self, request):
         return request.param
 
     @pytest.fixture(params=[4, 10])
@@ -134,16 +134,16 @@ def test_align_waveforms(
         self,
         sample_rate,
         waveform_duration,
-        coalescence_time,
+        right_pad,
         dummy_signal,
     ):
-        if coalescence_time == waveform_duration:
-            coalescence_time -= 1 / sample_rate
+        if right_pad == 0:
+            right_pad += 1 / sample_rate
         gen = _WaveformGenerator(
             waveform_approximant="",
             sample_rate=sample_rate,
             waveform_duration=waveform_duration,
-            coalescence_time=coalescence_time,
+            right_pad=right_pad,
             minimum_frequency=20,
             reference_frequency=20,
         )
@@ -151,9 +151,8 @@ def test_align_waveforms(
         t_final = dummy_signal.shape[-1] // 2 / sample_rate
         waveforms = gen.align_waveforms(dummy_signal, t_final)
         assert waveforms.shape[-1] == int(sample_rate * waveform_duration)
-        assert (
-            np.argmax(waveforms)
-            == (coalescence_time % waveform_duration) * sample_rate
+        assert np.argmax(waveforms) == int(
+            (waveform_duration - right_pad) * sample_rate
         )
 
 
@@ -192,7 +191,7 @@ def ligo_response_set(self, response_set_cls, duration, sample_rate, N):
                 sample_rate=sample_rate,
                 duration=duration,
                 num_injections=N,
-                coalescence_time=duration / 2,
+                right_pad=duration / 2,
                 **kwargs,
             )
         assert str(exc.value).startswith("Specified waveform duration")
@@ -205,7 +204,7 @@ def ligo_response_set(self, response_set_cls, duration, sample_rate, N):
                 sample_rate=sample_rate,
                 duration=duration,
                 num_injections=N - 1,
-                coalescence_time=duration / 2,
+                right_pad=duration / 2,
                 **kwargs,
             )
         assert str(exc.value).startswith("LigoResponseSet")
@@ -214,7 +213,7 @@ def ligo_response_set(self, response_set_cls, duration, sample_rate, N):
             sample_rate=sample_rate,
             duration=duration,
             num_injections=N,
-            coalescence_time=duration / 2,
+            right_pad=duration / 2,
             **kwargs,
         )
 
@@ -320,7 +319,7 @@ def test_read_with_shifts(self, ligo_response_set, tmp_path, N):
         # now try with times
         ligo_response_set.injection_time = np.arange(N) % (N // 2)
         ligo_response_set.duration = 2
-        ligo_response_set.coalescence_time = ligo_response_set.duration / 2
+        ligo_response_set.right_pad = ligo_response_set.duration / 2
         for ifo in "hl":
             key = f"{ifo}1"
             old = getattr(ligo_response_set, key)
diff --git a/libs/ledger/uv.lock b/libs/ledger/uv.lock
index bc2d52714..811da5aa7 100644
--- a/libs/ledger/uv.lock
+++ b/libs/ledger/uv.lock
@@ -3614,12 +3614,15 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "h5py", specifier = ">=3.6,<4.0" },
+    { name = "h5py", specifier = "~=3.6" },
     { name = "ml4gw", specifier = ">=0.7.2" },
-    { name = "numpy", specifier = ">=1.26.4,<2.0.0" },
+    { name = "numpy", specifier = ">=1.26.4,<2" },
     { name = "s3fs", specifier = ">=2024,<2025" },
 ]
 
+[package.metadata.requires-dev]
+dev = [{ name = "pytest", specifier = ">=8.3.0,<9" }]
+
 [[package]]
 name = "wadler-lindig"
 version = "0.1.4"
diff --git a/libs/utils/utils/preprocessing.py b/libs/utils/utils/preprocessing.py
index 19160fd33..e06e6c788 100644
--- a/libs/utils/utils/preprocessing.py
+++ b/libs/utils/utils/preprocessing.py
@@ -148,8 +148,8 @@ def forward(self, x: Tensor) -> Tensor:
                 "but found shape {}".format(x.shape)
             )
 
-        x, psd = self.psd_estimator(x)
-        whitened = self.whitener(x.double(), psd)
+        x, psd = self.psd_estimator(x.double())
+        whitened = self.whitener(x, psd)
 
         # unfold x and then put it into the expected shape.
         # Note that if x has both signal and background
diff --git a/projects/data/data/waveforms/rejection.py b/projects/data/data/waveforms/rejection.py
index ad4b0ec52..acd36c5fe 100644
--- a/projects/data/data/waveforms/rejection.py
+++ b/projects/data/data/waveforms/rejection.py
@@ -25,7 +25,7 @@ def rejection_sample(
     sample_rate: float,
     waveform_duration: float,
     waveform_approximant: str,
-    coalescence_time: float,
+    right_pad: float,
     highpass: float,
     lowpass: float,
     snr_threshold: float,
@@ -70,7 +70,7 @@ def rejection_sample(
             sample_rate,
             waveform_duration,
             waveform_approximant,
-            coalescence_time,
+            right_pad,
         )
         polarizations = {
             "cross": torch.Tensor(polarization_set.cross),
@@ -145,7 +145,7 @@ def rejection_sample(
         idx += num_accepted
         num_signals -= num_accepted
 
-    parameters["coalescence_time"] = coalescence_time
+    parameters["right_pad"] = right_pad
     parameters["sample_rate"] = sample_rate
     parameters["duration"] = waveform_duration
     parameters["num_injections"] = num_injections
diff --git a/projects/data/data/waveforms/testing.py b/projects/data/data/waveforms/testing.py
index a9bfb2f92..f71a98012 100644
--- a/projects/data/data/waveforms/testing.py
+++ b/projects/data/data/waveforms/testing.py
@@ -23,7 +23,7 @@ def testing_waveforms(
     sample_rate: float,
     waveform_duration: float,
     waveform_approximant: str,
-    coalescence_time: float,
+    right_pad: float,
     highpass: float,
     lowpass: float,
     snr_threshold: float,
@@ -71,9 +71,10 @@ def testing_waveforms(
             Duration of waveform in seconds
         waveform_approximant:
             Name of the waveform approximant to use.
-        coalescence_time:
+        right_pad:
             Location of the defining point of the signal within
-            the generated waveform
+            the generated waveform relative to the right edge
+            of the waveform (in seconds).
         highpass:
             The frequency to use for a highpass filter, specified
             in Hz
@@ -140,7 +141,7 @@ def testing_waveforms(
         sample_rate,
         waveform_duration,
         waveform_approximant,
-        coalescence_time,
+        right_pad,
         highpass,
         lowpass,
         snr_threshold,
diff --git a/projects/data/data/waveforms/training.py b/projects/data/data/waveforms/training.py
index dd047f7f6..4441f1307 100644
--- a/projects/data/data/waveforms/training.py
+++ b/projects/data/data/waveforms/training.py
@@ -14,7 +14,7 @@ def training_waveforms(
     minimum_frequency: float,
     reference_frequency: float,
     waveform_approximant: str,
-    coalescence_time: float,
+    right_pad: float,
 ):
     """
     Generates random training waveforms polarizations from a
@@ -39,9 +39,10 @@ def training_waveforms(
             reference to
         waveform_approximant:
             Name of the waveform approximant to use.
-        coalescence_time:
+        right_pad:
             Location of the defining point of the signal within
-            the generated waveform
+            the generated waveform relative to the right edge
+            of the waveform (in seconds).
 
     Returns:
         An IntrinsicParameterSet generated from the sampled parameters
@@ -59,7 +60,7 @@ def training_waveforms(
         sample_rate,
         waveform_duration,
         waveform_approximant,
-        coalescence_time,
+        right_pad,
     )
     return waveforms
 
diff --git a/projects/train/apptainer.def b/projects/train/apptainer.def
index ce8b0dd2f..283c97ceb 100644
--- a/projects/train/apptainer.def
+++ b/projects/train/apptainer.def
@@ -10,6 +10,7 @@ From: ghcr.io/astral-sh/uv:0.6.10-python3.10-bookworm-slim
 ../../aframe /opt/aframe/aframe
 ../../pyproject.toml /opt/aframe/pyproject.toml
 ../../libs/ledger /opt/aframe/libs/ledger
+../../libs/priors /opt/aframe/libs/priors
 ../../libs/architectures /opt/aframe/libs/architectures
 
 %post
diff --git a/projects/train/configs/bbh.yaml b/projects/train/configs/bbh.yaml
index 350dccd7d..28ca602b3 100644
--- a/projects/train/configs/bbh.yaml
+++ b/projects/train/configs/bbh.yaml
@@ -68,7 +68,6 @@ data:
          # alpha: -3
          # decay_steps: 989
     # validation args
-    valid_frac: 0.25
     valid_stride: 0.5
     num_valid_views: 5
     valid_livetime: 57600 
diff --git a/projects/train/configs/bns.yaml b/projects/train/configs/bns.yaml
index acc773d30..f740e3089 100644
--- a/projects/train/configs/bns.yaml
+++ b/projects/train/configs/bns.yaml
@@ -72,7 +72,6 @@ data:
          # alpha: -3
          # decay_steps: 989
     # validation args
-    valid_frac: 0.25
     valid_stride: 0.5
     num_valid_views: 5
     valid_livetime: 57600 
diff --git a/projects/train/configs/training_prior.yaml b/projects/train/configs/training_prior.yaml
new file mode 100644
index 000000000..12c66b5f6
--- /dev/null
+++ b/projects/train/configs/training_prior.yaml
@@ -0,0 +1,72 @@
+# default prior for use with `IMRPhenomPv2`
+class_path: train.prior.AframePrior
+init_args:
+  conversion_function: train.conversion.precessing_to_lalsimulation_parameters
+  priors:
+    chirp_mass: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 5
+        high: 250
+        validate_args: false
+    mass_ratio: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0.05 
+        high: 0.999
+        validate_args: false
+    distance: 
+      class_path: ml4gw.distributions.UniformComovingVolume
+      init_args:
+        minimum:  0 
+        maximum: 2
+        distance_type: redshift
+        validate_args: false
+    inclination: 
+      class_path: ml4gw.distributions.Sine
+      init_args:
+        low: 0 
+        high: 3.14159 
+        validate_args: false
+    phic: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0.0 
+        high: 6.28318 
+        validate_args: false
+    a_1: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0.0 
+        high: 0.999
+        validate_args: false
+    a_2: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0.0 
+        high: 0.999
+        validate_args: false
+    tilt_1: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0.0 
+        high: 3.14159  
+        validate_args: false
+    tilt_2: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0.0 
+        high: 3.14159 
+        validate_args: false
+    phi_jl: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0.0 
+        high: 6.28318 
+        validate_args: false
+    phi_12: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0.0
+        high: 6.28318 
+        validate_args: false
diff --git a/projects/train/pyproject.toml b/projects/train/pyproject.toml
index b7492391d..8adbbc07e 100644
--- a/projects/train/pyproject.toml
+++ b/projects/train/pyproject.toml
@@ -22,9 +22,10 @@ dependencies = [
     "fsspec[s3]>=2024,<2025",
     "urllib3>=1.25.4,<1.27",
     "utils",
-    "ml4gw>=0.7.2",
+    "ml4gw>=0.7.7",
     "aframe",
     "ledger",
+    "priors",
     "architectures",
     "bayesian-optimization>=1.4.3,<2",
     "filelock>=3.13.1,<4",
@@ -36,7 +37,10 @@ dependencies = [
 train = "train.cli:main"
 
 [dependency-groups]
-dev = ["pytest~=7.3"]
+dev = [
+    "pytest~=7.3",
+    "jupyter>=1.0.0",    
+]
 
 [tool.uv]
 
@@ -49,6 +53,7 @@ explicit = true
 utils = { path = "../../libs/utils", editable = true }
 aframe = { path = "../..", editable = true }
 ledger = { path = "../../libs/ledger", editable = true }
+priors = { path = "../../libs/priors", editable = true }
 architectures = { path = "../../libs/architectures", editable = true }
 
 [build-system]
diff --git a/projects/train/train.yaml b/projects/train/train.yaml
index 9f7f422d7..fd19c4a0c 100644
--- a/projects/train/train.yaml
+++ b/projects/train/train.yaml
@@ -34,12 +34,15 @@ data:
     # data_dir:
     # ifos:
 
-    # preprocessing args
+    # dataloading args
     batch_size: 384
     batches_per_epoch: 3700
     num_files_per_batch: 10
-    chunk_size: 10000
+    # Used for loading waveforms from disk
     chunks_per_epoch: 10
+    chunk_size: 10000
+
+    # preprocessing args
     # kernel_length:
     psd_length: 8
     # fduration:
@@ -68,8 +71,37 @@ data:
          # max_snr: 100
          # alpha: -3
          # decay_steps: 989
+    # If loading waveforms from disk, waveform_sampler should be
+    # an instance of train.data.waveforms.WaveformLoader
+    waveform_sampler:
+      class_path: train.data.waveforms.WaveformLoader
+      init_args:
+        training_waveform_path: ${oc.env:AFRAME_TRAIN_DATA_DIR}/training_waveforms.hdf5
+    # waveform_sampler: 
+    #   class_path: train.data.waveforms.generator.cbc.CBCGenerator
+    #   init_args:
+    #     training_prior: ./training_prior.yaml
+    #     val_waveform_file: ${oc.env:AFRAME_TRAIN_DATA_DIR}/val_waveforms.hdf5
+    #     approximant: ml4gw.waveforms.IMRPhenomPv2
+    #     f_min: 20
+    #     f_ref: 40
+    #     duration: 8
+    # Extrinsic parameter distributions
+    dec: 
+      class_path: ml4gw.distributions.Cosine
+    psi: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0
+        high: 3.14159
+        validate_args: false
+    phi: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0
+        high: 6.28318
+        validate_args: false
     # validation args
-    valid_frac: 0.25
     valid_stride: 0.5
     num_valid_views: 5
     valid_livetime: 57600 
@@ -110,7 +142,7 @@ trainer:
   # strategy: set to ddp if len(devices) > 1
   #precision: 16-mixed
   accelerator: auto
-  max_epochs: 400
+  max_epochs: 200
   check_val_every_n_epoch: 1
   log_every_n_steps: 20
   benchmark: true
diff --git a/projects/train/train/augmentations.py b/projects/train/train/augmentations.py
index 345625339..9b272caee 100644
--- a/projects/train/train/augmentations.py
+++ b/projects/train/train/augmentations.py
@@ -55,7 +55,9 @@ def forward(self, X):
         if num > 0:
             channel = torch.randint(X.shape[1], size=(num,))
             indices = torch.randint(X.shape[0], size=(num,))
-            X[indices, channel] = torch.zeros(X.shape[-1], device=X.device)
+            X[indices, channel] = torch.zeros(
+                X.shape[-1], device=X.device, dtype=X.dtype
+            )
 
         return X, indices
 
diff --git a/projects/train/train/callbacks.py b/projects/train/train/callbacks.py
index b23a42330..40fee25ac 100644
--- a/projects/train/train/callbacks.py
+++ b/projects/train/train/callbacks.py
@@ -43,9 +43,16 @@ def on_train_end(self, trainer, pl_module):
         )
 
         device = pl_module.device
-        [X], waveforms = next(iter(trainer.train_dataloader))
-        X = X.to(device)
-        X, y = trainer.datamodule.augment(X, waveforms)
+        # Handle the case of loading training waveforms from disk
+        if trainer.datamodule.waveforms_from_disk:
+            [X], waveforms = next(iter(trainer.train_dataloader))
+            X = X.to(device)
+            waveforms = waveforms.to(device)
+            X, y = trainer.datamodule.inject(X, waveforms)
+        else:
+            [X] = next(iter(trainer.train_dataloader))
+            X = X.to(device)
+            X, y = trainer.datamodule.inject(X)
         if isinstance(X, tuple):
             X = tuple(i.cpu() for i in X)
         else:
@@ -75,11 +82,16 @@ def on_train_start(self, trainer, pl_module):
             save_dir = trainer.logger.save_dir
 
             # build training batch by hand
-            [X], waveforms = next(iter(trainer.train_dataloader))
-            waveforms = trainer.datamodule.slice_waveforms(waveforms)
-            X = X.to(device)
-
-            X, y = trainer.datamodule.augment(X, waveforms)
+            # Handle the case of loading training waveforms from disk
+            if trainer.datamodule.waveforms_from_disk:
+                [X], waveforms = next(iter(trainer.train_dataloader))
+                X = X.to(device)
+                waveforms = waveforms.to(device)
+                X, y = trainer.datamodule.inject(X, waveforms)
+            else:
+                [X] = next(iter(trainer.train_dataloader))
+                X = X.to(device)
+                X, y = trainer.datamodule.inject(X)
             # If X is not a tuple, make it one for consistency
             # of format for saving to file below
             if not isinstance(X, tuple):
diff --git a/projects/train/train/cli.py b/projects/train/train/cli.py
index 2d96632f3..b43e65467 100644
--- a/projects/train/train/cli.py
+++ b/projects/train/train/cli.py
@@ -9,6 +9,7 @@ class AframeCLI(LightningCLI):
     def __init__(self, *args, **kwargs):
         # hack into init to hardcode
         # the WandbSaveConfig callback
+        kwargs["parser_kwargs"] = {"parser_mode": "omegaconf"}
         kwargs["save_config_callback"] = WandbSaveConfig
         super().__init__(*args, **kwargs)
 
@@ -38,6 +39,24 @@ def add_arguments_to_parser(self, parser):
             "model.init_args.metric.init_args.stride",
         )
 
+        parser.link_arguments(
+            "data.init_args.sample_rate",
+            "data.init_args.waveform_sampler.init_args.sample_rate",
+            apply_on="parse",
+        )
+
+        parser.link_arguments(
+            "data.init_args.fduration",
+            "data.init_args.waveform_sampler.init_args.fduration",
+            apply_on="parse",
+        )
+
+        parser.link_arguments(
+            "data.init_args.ifos",
+            "data.init_args.waveform_sampler.init_args.ifos",
+            apply_on="parse",
+        )
+
 
 def main(args=None):
     cli = AframeCLI(
diff --git a/projects/train/train/constraints.py b/projects/train/train/constraints.py
new file mode 100644
index 000000000..aa2806a96
--- /dev/null
+++ b/projects/train/train/constraints.py
@@ -0,0 +1,18 @@
+import torch
+
+
+def mass_ratio_constraint(
+    parameters: dict[str, torch.Tensor],
+) -> dict[str, torch.Tensor]:
+    """
+    Enforce mass_2 < mass_1. Assumes that the given parameter dictionary
+    already contains the component masses
+    """
+    try:
+        mask = parameters["mass_1"] > parameters["mass_2"]
+    except KeyError as exc:
+        raise ValueError(
+            "Parameter dictionary did not contain component masses"
+        ) from exc
+
+    return {key: parameters[key][mask] for key in parameters.keys()}
diff --git a/projects/train/train/conversion.py b/projects/train/train/conversion.py
new file mode 100644
index 000000000..c3e5e594a
--- /dev/null
+++ b/projects/train/train/conversion.py
@@ -0,0 +1,88 @@
+import torch
+from ml4gw.waveforms.conversion import (
+    bilby_spins_to_lalsim,
+    chirp_mass_and_mass_ratio_to_components,
+)
+
+
+def add_mass_params(
+    parameters: dict[str, torch.Tensor],
+) -> dict[str, torch.Tensor]:
+    """
+    Add mass_1 and mass_2 to parameter dictionary if
+    chirp_mass and mass_ratio are present, or vice-versa.
+
+    Raises a ValueError if neither pair are present.
+    """
+    if "chirp_mass" in parameters and "mass_ratio" in parameters:
+        mass_1, mass_2 = chirp_mass_and_mass_ratio_to_components(
+            parameters["chirp_mass"], parameters["mass_ratio"]
+        )
+
+        parameters["mass_1"] = mass_1
+        parameters["mass_2"] = mass_2
+    elif "mass_1" in parameters and "mass_2" in parameters:
+        m1, m2 = parameters["mass_1"], parameters["mass_2"]
+        parameters["chirp_mass"] = (m1 * m2) ** (3 / 5) / (m1 + m2) ** (1 / 5)
+        parameters["mass_ratio"] = m2 / m1
+    else:
+        raise ValueError(
+            "Parameter dictionary did not contain either "
+            "(chirp mass, mass ratio) or (mass 1, mass 2)"
+        )
+    return parameters
+
+
+def precessing_to_lalsimulation_parameters(
+    parameters: dict[str, torch.Tensor],
+) -> dict[str, torch.Tensor]:
+    """
+    Convert precessing spin parameters to lalsimulation parameters
+    """
+    parameters = add_mass_params(parameters)
+    mass_1 = parameters["mass_1"]
+
+    # TODO: hard coding f_ref = 40 here b/c not sure best way to link this
+    # to the f_ref specified in the config file
+    incl, s1x, s1y, s1z, s2x, s2y, s2z = bilby_spins_to_lalsim(
+        parameters["inclination"],
+        parameters["phi_jl"],
+        parameters["tilt_1"],
+        parameters["tilt_2"],
+        parameters["phi_12"],
+        parameters["a_1"],
+        parameters["a_2"],
+        parameters["mass_1"],
+        parameters["mass_2"],
+        40,
+        torch.zeros(len(mass_1), device=mass_1.device),
+    )
+
+    parameters["s1x"] = s1x
+    parameters["s1y"] = s1y
+    parameters["s1z"] = s1z
+    parameters["s2x"] = s2x
+    parameters["s2y"] = s2y
+    parameters["s2z"] = s2z
+    parameters["inclination"] = incl
+    return parameters
+
+
+def aligned_to_lalsimulation_parameters(
+    parameters: dict[str, torch.Tensor],
+) -> dict[str, torch.Tensor]:
+    """
+    Convert aligned spin parameters to lalsimulation parameters
+    """
+    parameters = add_mass_params(parameters)
+    mass_1 = parameters["mass_1"]
+
+    parameters["s1x"] = torch.zeros_like(mass_1)
+    parameters["s1y"] = torch.zeros_like(mass_1)
+
+    parameters["s2x"] = torch.zeros_like(mass_1)
+    parameters["s2y"] = torch.zeros_like(mass_1)
+
+    parameters["s1z"] = parameters["chi1"]
+    parameters["s2z"] = parameters["chi2"]
+    return parameters
diff --git a/projects/train/train/data/base.py b/projects/train/train/data/base.py
index b7b27b37f..cfd7d7638 100644
--- a/projects/train/train/data/base.py
+++ b/projects/train/train/data/base.py
@@ -14,19 +14,19 @@
 from ml4gw.transforms import Whiten
 from ml4gw.utils.slicing import unfold_windows
 
-from ledger.injections import WaveformSet, waveform_class_factory
 from train import augmentations as aug
 from train.data.utils import fs as fs_utils
 from train.metrics import get_timeslides
-from train.waveform_sampler import (
+from train.data.waveforms import (
     ChunkedWaveformDataset,
     Hdf5WaveformLoader,
+    WaveformLoader,
     WaveformSampler,
 )
-from utils import x_per_y
 from utils.preprocessing import PsdEstimator
 
 Tensor = torch.Tensor
+Distribution = torch.distributions.Distribution
 TransformedDist = torch.distributions.TransformedDistribution
 
 
@@ -56,15 +56,104 @@ def __iter__(self):
 
 
 class BaseAframeDataset(pl.LightningDataModule):
+    """
+    Base LightningDataModule for loading data to train Aframe models.
+    Subclasses must define the `inject` method
+    which encodes how background strain,
+    cross/plus polarizations and parameters
+    are processed before being passed to a model
+
+    Args:
+        data_dir:
+            Path to the directory containing the training data.
+            If this is a s3 bucket, it will be downloaded
+            to a local directory.
+        ifos:
+            List of interferometers to use for training.
+        sample_rate:
+            Sample rate in Hz of the training data.
+        dec:
+            The distribution of declinations to sample from
+        psi:
+            The distribution of polarization angles to sample from
+        phi:
+            The distribution of "right ascensions" to sample from
+        batches_per_epoch:
+            Number of batches per epoch.
+        num_files_per_batch:
+            Number of background files to sample from each batch.
+        waveform_sampler:
+            `WaveformSampler` object that produces waveforms and parameters
+            for training and validation. See `train.data.waveforms.sampler`
+            for methods this object should define.
+        max_num_workers:
+            Maximum number of workers to assign to each
+            training dataloader.
+        batch_size:
+            Batch size for training.
+        kernel_length:
+            Length in seconds of the time-domain kernel
+            passed to the model after whitening.
+        fduration:
+            The length of the whitening filter's impulse
+            response, in seconds. `fduration / 2` seconds
+            worth of data will be cropped from the edges
+            of the whitened timeseries.
+        psd_length:
+            Length in seconds of the PSD used for whitening.
+        waveform_prob:
+            Probability that a batch element will contain a waveform.
+        left_pad:
+            Length in seconds of the minimum gap between the left
+            edge of whitened kernel and coalescence point of the signal.
+        right_pad:
+            Length in seconds of the minimum gap between the right
+            edge of whitened kernel and coalescence point of the signal.
+        fftlength:
+            Length in seconds of the FFT used for PSD estimation.
+            If `None`, will use the length of the kernel + fduration.
+        highpass:
+            Highpass filter frequency in Hz.
+        lowpass:
+            Lowpass filter frequency in Hz.
+        snr_sampler:
+            A callable that samples SNRs for the injected signals.
+            If `None`, SNRs will be left unchanged.
+        valid_stride:
+            Stride in seconds for the validation timeslides.
+            If `None`, will use `kernel_length + fduration`.
+        num_valid_views:
+            Number of views to inject into the validation data.
+            Each view will contain the same signal at a different
+            position in the background.
+        min_valid_duration:
+            Minimum duration in seconds of the validation data.
+        valid_livetime:
+            Total livetime in seconds of the validation data
+            to be generated via timeslides.
+        chunks_per_epoch:
+            Number of chunks of waveforms to load from disk
+            each epoch. Not used if generating waveforms
+            during training.
+        chunk_size:
+            Number of waveforms to load in each chunk.
+            Not used if generating waveforms during training.
+        verbose:
+            Whether to log debug information during training.
+    """
+
     def __init__(
         self,
         # data loading args
         data_dir: str,
         ifos: Sequence[str],
         sample_rate: float,
-        valid_frac: float,
+        dec: Distribution,
+        psi: Distribution,
+        phi: Distribution,
         batches_per_epoch: int,
         num_files_per_batch: int,
+        waveform_sampler: WaveformSampler,
         # preprocessing args
         batch_size: int,
         kernel_length: float,
@@ -72,8 +161,6 @@ def __init__(
         psd_length: float,
         # augmentation args
         waveform_prob: float = 1,
-        max_snr: float = 100,
-        snr_alpha: float = 3,
         left_pad: float = 0,
         right_pad: float = 0,
         fftlength: Optional[float] = None,
@@ -87,15 +174,17 @@ def __init__(
         num_valid_views: int = 4,
         min_valid_duration: float = 15000,
         valid_livetime: float = (3600 * 12),
-        verbose: bool = False,
-        # waveform dataloader args
+        max_num_workers: int = 6,
+        # dataloading args
         chunks_per_epoch: int = 1,
         chunk_size: int = 10000,
+        verbose: bool = False,
     ) -> None:
         super().__init__()
         self.init_logging(verbose)
         self.num_ifos = len(ifos)
-        self.save_hyperparameters()
+        self.max_num_workers = max_num_workers
+        self.save_hyperparameters(ignore=["waveform_sampler"])
 
         # Set up some of our data augmentation modules
         self.inverter = SignalInverter(0.5)
@@ -105,11 +194,17 @@ def __init__(
         # downloaded first, either for loading signals
         # or to infer sample rate, so wait to construct
         # them until self.setup
-        self.waveform_sampler = None
         self.whitener = None
         self.projector = None
         self.psd_estimator = None
         self._on_device = False
+
+        self.dec, self.psi, self.phi = dec, psi, phi
+        self.waveform_sampler = waveform_sampler
+        # If we're using a `WaveformLoader`, we're loading
+        # training waveforms from disk, so have a flag tp
+        # indicate that
+        self.waveforms_from_disk = isinstance(waveform_sampler, WaveformLoader)
         self.snr_sampler = snr_sampler
 
         # generate our local node data directory
@@ -198,17 +293,6 @@ def right_pad_size(self) -> int:
         """
         return int(self.hparams.right_pad * self.hparams.sample_rate)
 
-    @property
-    def train_waveform_fnames(self) -> Sequence[str]:
-        data_dir = os.path.join(self.data_dir, "training_waveforms")
-        fnames = glob.glob(f"{data_dir}/waveforms*.hdf5")
-        return list(fnames)
-
-    @property
-    def signal_time(self):
-        with h5py.File(self.train_waveform_fnames[0], "r") as f:
-            return f.attrs["coalescence_time"]
-
     def train_val_split(self) -> tuple[Sequence[str], Sequence[str]]:
         fnames = glob.glob(f"{self.data_dir}/background/*.hdf5")
         fnames = sorted([Path(fname) for fname in fnames])
@@ -228,19 +312,17 @@ def val_batch_size(self):
         """Use larger batch sizes when we don't need gradients."""
         return int(1 * self.hparams.batch_size)
 
+    @property
+    def num_workers(self):
+        local_world_size = len(self.trainer.device_ids)
+        return min(
+            self.max_num_workers, int(os.cpu_count() / local_world_size)
+        )
+
     # ================================================ #
     # Utilities for initial data loading and preparation
     # ================================================ #
 
-    @property
-    def waveform_set_cls(self):
-        cls = waveform_class_factory(
-            self.hparams.ifos,
-            WaveformSet,
-            "WaveformSet",
-        )
-        return cls
-
     def prepare_data(self):
         """
         Download s3 data if it doesn't exist.
@@ -261,7 +343,9 @@ def slice_waveforms(self, waveforms: torch.Tensor) -> torch.Tensor:
         Slice waveforms to the correct length depending on
         requested left and right padding
         """
-        signal_idx = int(self.signal_time * self.hparams.sample_rate)
+        signal_idx = waveforms.shape[-1] - int(
+            self.waveform_sampler.right_pad * self.hparams.sample_rate
+        )
         kernel_size = int(
             self.hparams.kernel_length * self.hparams.sample_rate
         )
@@ -288,37 +372,6 @@ def slice_waveforms(self, waveforms: torch.Tensor) -> torch.Tensor:
 
         return waveforms
 
-    def get_slice_bounds(self, total, world_size, rank) -> tuple[int, int]:
-        """
-        Figure which chunk of waveforms we should be
-        slicing given our rank and world size
-        """
-        per_dev = x_per_y(abs(total), world_size)
-        start = rank * per_dev
-        stop = (rank + 1) * per_dev
-        return start, stop
-
-    def load_val_waveforms(self, f, world_size, rank):
-        waveform_set = self.waveform_set_cls.read(f)
-
-        if waveform_set.coalescence_time != self.signal_time:
-            raise ValueError(
-                "Training waveforms and validation waveforms have different "
-                f"signal times, got {self.signal_time} and "
-                f"{waveform_set.coalescence_time}, respectively"
-            )
-
-        length = len(waveform_set.waveforms)
-
-        if not rank:
-            self._logger.info(f"Validating on {length} waveforms")
-        stop, start = self.get_slice_bounds(length, world_size, rank)
-
-        self._logger.info(f"Loading {start - stop} validation signals")
-        start, stop = -start, -stop or None
-        waveforms = torch.Tensor(waveform_set.waveforms[start:stop])
-        return waveforms
-
     def load_val_background(self, fnames: list[str]):
         self._logger.info("Loading validation background data")
         val_background = []
@@ -366,6 +419,16 @@ def build_transforms(self):
             self.hparams.lowpass,
         )
 
+    def sample_extrinsic(self, X: torch.Tensor):
+        """
+        Sample extrinsic parameters used to project waveforms
+        """
+        N = len(X)
+        dec = self.dec.sample((N,)).to(X.device)
+        psi = self.psi.sample((N,)).to(X.device)
+        phi = self.phi.sample((N,)).to(X.device)
+        return dec, psi, phi
+
     def setup(self, stage: str) -> None:
         world_size, rank = self.get_world_size_and_rank()
         self._logger = self.get_logger(world_size, rank)
@@ -381,12 +444,6 @@ def setup(self, stage: str) -> None:
 
         self._logger.info(f"Validated sample rate {sample_rate}")
 
-        # now define some of the augmentation transforms
-        # that require sample rate information
-        self._logger.info("Constructing sample rate dependent transforms")
-        self.build_transforms()
-        self.transforms_to_device()
-
         # load in our validation background up front and
         # compute which timeslides we'll do on this device
         # if we're doing distributed training so we'll know
@@ -406,14 +463,20 @@ def setup(self, stage: str) -> None:
             self.val_batch_size,
         )
 
-        self.waveform_sampler = WaveformSampler()
-
-        val_waveform_file = os.path.join(self.data_dir, "val_waveforms.hdf5")
-        self.val_waveforms = self.load_val_waveforms(
-            val_waveform_file, world_size, rank
+        self.val_waveforms = self.waveform_sampler.get_val_waveforms(
+            world_size, rank
+        )
+        self.waveform_sampler.get_train_waveforms(
+            world_size, rank, self.device
         )
         self._logger.info("Initial dataloading complete")
 
+        # now define some of the augmentation transforms
+        # that require sample rate information
+        self._logger.info("Constructing sample rate dependent transforms")
+        self.build_transforms()
+        self.transforms_to_device()
+
     # ================================================ #
     # Utilities for doing augmentation/preprocessing
     # after tensors have been transferred to GPU
@@ -426,11 +489,12 @@ def device(self):
     def on_before_batch_transfer(self, batch, _):
         """
         Slice loaded waveforms before sending to device
+        if not generating waveforms during training
         """
         # TODO: maybe pass indices as argument to
         # waveform loader to reduce quantity of data
         # we need to load
-        if self.trainer.training:
+        if self.trainer.training and self.waveforms_from_disk:
             X, waveforms = batch
             waveforms = self.slice_waveforms(waveforms)
             batch = X, waveforms
@@ -447,8 +511,12 @@ def on_after_batch_transfer(self, batch, _):
         if self.trainer.training:
             # if we're training, perform random augmentations
             # on input data and use it to impact labels
-            [X], waveforms = batch
-            batch = self.augment(X, waveforms)
+            if self.waveforms_from_disk:
+                [batch], waveforms = batch
+                batch = self.inject(batch, waveforms)
+            else:
+                [batch] = batch
+                batch = self.inject(batch)
         elif self.trainer.validating or self.trainer.sanity_checking:
             # If we're in validation mode but we're not validating
             # on the local device, the relevant tensors will be
@@ -467,7 +535,7 @@ def on_after_batch_transfer(self, batch, _):
         return batch
 
     @torch.no_grad()
-    def augment(self, X):
+    def inject(self, X):
         """
         Override this in child classes to define
         application-specific augmentations
@@ -479,7 +547,9 @@ def augment(self, X):
     # ================================================ #
     @torch.no_grad()
     def build_val_batches(
-        self, background: Tensor, signals: Tensor
+        self,
+        background: Tensor,
+        signals: Tensor,
     ) -> tuple[Tensor, Tensor, Tensor]:
         """
         Unfold a timeseries of background data
@@ -502,6 +572,7 @@ def build_val_batches(
 
         # split data into kernel and psd data and estimate psd
         X, psd = self.psd_estimator(background)
+
         # sometimes at the end of a segment, there won't be
         # enough background kernels and so we'll have to inject
         # our signals on overlapping data and ditch some at the end
@@ -516,7 +587,9 @@ def build_val_batches(
         # the background, each showing a different, overlapping
         # portion of the signal
         kernel_size = X.size(-1)
-        signal_idx = int(self.signal_time * self.hparams.sample_rate)
+        signal_idx = signals.shape[-1] - int(
+            self.waveform_sampler.right_pad * self.hparams.sample_rate
+        )
         max_start = int(
             signal_idx - self.left_pad_size - self.filter_size // 2
         )
@@ -558,8 +631,6 @@ def val_dataloader(self) -> ZippedDataset:
         # throughout all those batches.
         num_waveforms = len(self.val_waveforms)
         signal_batch_size = (num_waveforms - 1) // self.valid_loader_length + 1
-
-        # self._logger.info(f"signal batch size: {signal_batch_size}")
         signal_dataset = torch.utils.data.TensorDataset(self.val_waveforms)
         signal_loader = torch.utils.data.DataLoader(
             signal_dataset,
@@ -575,10 +646,6 @@ def val_dataloader(self) -> ZippedDataset:
         return dataset
 
     def train_dataloader(self) -> torch.utils.data.DataLoader:
-        # divide batches per epoch up among all devices
-        world_size, _ = self.get_world_size_and_rank()
-        batches_per_epoch = self.hparams.batches_per_epoch // world_size
-
         # build our strain dataset and dataloader
         dataset = Hdf5TimeSeriesDataset(
             self.train_fnames,
@@ -593,23 +660,25 @@ def train_dataloader(self) -> torch.utils.data.DataLoader:
         pin_memory = isinstance(
             self.trainer.accelerator, pl.accelerators.CUDAAccelerator
         )
-        # multiprocess data loading
-        local_world_size = len(self.trainer.device_ids)
-        num_workers = min(6, int(os.cpu_count() / local_world_size))
         self._logger.debug(
-            f"Using {num_workers} workers for strain data loading"
+            f"Using {self.num_workers} workers for strain data loading"
         )
         dataloader = torch.utils.data.DataLoader(
             dataset,
-            num_workers=num_workers,
+            num_workers=self.num_workers,
             pin_memory=pin_memory,
         )
 
+        # If we're not loading waveforms from disk, just return
+        # the background dataloader
+        if not self.waveforms_from_disk:
+            return dataloader
+
         # build iterator for waveform loading
         # that will load chunks of waveforms
         # to be sampled from
         waveform_loader = Hdf5WaveformLoader(
-            self.train_waveform_fnames,
+            self.waveform_sampler.training_waveform_files,
             batch_size=self.hparams.chunk_size,
             batches_per_epoch=self.hparams.chunks_per_epoch or 1,
             channels=["cross", "plus"],
@@ -617,6 +686,8 @@ def train_dataloader(self) -> torch.utils.data.DataLoader:
         )
         # calculate how many batches we'll sample from each chunk
         # based on requested chunks per epoch and batches per epoch
+        world_size, _ = self.get_world_size_and_rank()
+        batches_per_epoch = self.hparams.batches_per_epoch // world_size
         batches_per_chunk = (
             int(batches_per_epoch // self.hparams.chunks_per_epoch) + 1
         )
diff --git a/projects/train/train/data/supervised/__init__.py b/projects/train/train/data/supervised/__init__.py
index 291ef42a8..4ca80c178 100644
--- a/projects/train/train/data/supervised/__init__.py
+++ b/projects/train/train/data/supervised/__init__.py
@@ -1,4 +1,4 @@
-from .frequency_domain import (
+from .time_frequency_domain import (
     FrequencyDomainSupervisedAframeDataset,
     SpectrogramDomainSupervisedAframeDataset,
 )
diff --git a/projects/train/train/data/supervised/multimodal.py b/projects/train/train/data/supervised/multimodal.py
index 06f48746f..034e396d7 100644
--- a/projects/train/train/data/supervised/multimodal.py
+++ b/projects/train/train/data/supervised/multimodal.py
@@ -25,39 +25,6 @@ def build_val_batches(self, background, signals):
 
         return (X_bg, X_bg_fft), (X_fg, X_fg_fft)
 
-    def on_after_batch_transfer(self, batch, _):
-        """
-        This is a method inherited from the DataModule
-        base class that gets called after data returned
-        by a dataloader gets put on the local device,
-        but before it gets passed to the LightningModule.
-        Use this to do on-device augmentation/preprocessing.
-        """
-        if self.trainer.training:
-            # if we're training, perform random augmentations
-            # on input data and use it to impact labels
-            [X], waveforms = batch
-            (X, X_fft), y = self.augment(X, waveforms)
-            batch = (X, X_fft, y)
-        elif self.trainer.validating or self.trainer.sanity_checking:
-            # If we're in validation mode but we're not validating
-            # on the local device, the relevant tensors will be
-            # empty, so just pass them through with a 0 shift to
-            # indicate that this should be ignored
-            [background, _, timeslide_idx], [signals] = batch
-
-            # If we're validating, unfold the background
-            # data into a batch of overlapping kernels now that
-            # we're on the GPU so that we're not transferring as
-            # much data from CPU to GPU. Once everything is
-            # on-device, pre-inject signals into background.
-            shift = self.timeslides[timeslide_idx].shift_size
-            (X_bg, X_bg_fft), (X_fg, X_fg_fft) = self.build_val_batches(
-                background, signals
-            )
-            batch = (shift, X_bg, X_fg, X_bg_fft, X_fg_fft)
-        return batch
-
     def compute_frequency_domain_data(self, X, psds):
         asds = psds**0.5
 
@@ -86,8 +53,8 @@ def compute_frequency_domain_data(self, X, psds):
 
         return X_fft
 
-    def augment(self, X, waveforms):
-        X, y, psds = super().augment(X, waveforms)
+    def inject(self, X, waveforms=None):
+        X, y, psds = super().inject(X, waveforms)
         X = self.whitener(X, psds)
         X_fft = self.compute_frequency_domain_data(X, psds)
 
diff --git a/projects/train/train/data/supervised/supervised.py b/projects/train/train/data/supervised/supervised.py
index 279d9d2f4..66be2c57c 100644
--- a/projects/train/train/data/supervised/supervised.py
+++ b/projects/train/train/data/supervised/supervised.py
@@ -43,19 +43,46 @@ def sample_prob(self):
         return self.hparams.waveform_prob + self.swap_prob + self.mute_prob
 
     @torch.no_grad()
-    def augment(self, X, waveforms):
+    def inject(self, X, waveforms=None):
+        if self.waveforms_from_disk and waveforms is None:
+            raise ValueError(
+                "Waveforms should be passed to the `inject` method "
+                "if waveforms are being loaded from disk, got None"
+            )
+
         X, psds = self.psd_estimator(X)
         X = self.inverter(X)
         X = self.reverser(X)
         # sample enough waveforms to do true injections,
         # swapping, and muting
 
-        *params, polarizations, mask = self.waveform_sampler(
-            X, self.sample_prob, waveforms
+        rvs = torch.rand(size=X.shape[:1], device=X.device)
+        mask = rvs < self.sample_prob
+
+        dec, psi, phi = self.sample_extrinsic(X[mask])
+        # If we're loading waveforms from disk, we can
+        # slice out the ones we want.
+        # If not, we're generating them on the fly.
+        if self.waveforms_from_disk:
+            # TODO: Can we just use `mask` to slice out the
+            # waveforms we want here? Copying this from the
+            # old `WaveformSampler` in case it handles edge
+            # cases I'm not thinking of
+            N = mask.sum().item()
+            idx = torch.randperm(waveforms.shape[0])[:N]
+            waveforms = waveforms[idx].to(X.device).float()
+            hc, hp = waveforms[:, 0], waveforms[:, 1]
+        else:
+            hc, hp = self.waveform_sampler.sample(X[mask])
+
+        snrs = self.snr_sampler.sample((mask.sum().item(),)).to(X.device)
+        responses = self.projector(
+            dec, psi, phi, snrs, psds[mask], cross=hc, plus=hp
         )
-        N = len(params[0])
-        snrs = self.snr_sampler.sample((N,)).to(X.device)
-        responses = self.projector(*params, snrs, psds[mask], **polarizations)
+        # If we're loading waveforms from disk, we'll have sliced
+        # the waveforms already in `on_before_batch_transfer`
+        if not self.waveforms_from_disk:
+            responses = self.slice_waveforms(responses)
         kernels = sample_kernels(
             responses, kernel_size=X.size(-1), coincident=True
         )
diff --git a/projects/train/train/data/supervised/time_domain.py b/projects/train/train/data/supervised/time_domain.py
index 73dce48ec..a26806614 100644
--- a/projects/train/train/data/supervised/time_domain.py
+++ b/projects/train/train/data/supervised/time_domain.py
@@ -16,7 +16,7 @@ def build_val_batches(self, background, signals):
         X_fg = torch.stack(X_fg)
         return X_bg, X_fg
 
-    def augment(self, X, waveforms):
-        X, y, psds = super().augment(X, waveforms)
+    def inject(self, X, waveforms=None):
+        X, y, psds = super().inject(X, waveforms)
         X = self.whitener(X, psds)
         return X, y
diff --git a/projects/train/train/data/supervised/frequency_domain.py b/projects/train/train/data/supervised/time_frequency_domain.py
similarity index 95%
rename from projects/train/train/data/supervised/frequency_domain.py
rename to projects/train/train/data/supervised/time_frequency_domain.py
index 80a187e4a..09c9f0486 100644
--- a/projects/train/train/data/supervised/frequency_domain.py
+++ b/projects/train/train/data/supervised/time_frequency_domain.py
@@ -23,8 +23,8 @@ def build_transforms(self, *args, **kwargs):
             spectrogram_shape=self.spectrogram_shape,
         )
 
-    def augment(self, X):
-        X, y, psds = super().augment(X)
+    def inject(self, X, waveforms=None):
+        X, y, psds = super().inject(X, waveforms)
         X = self.whitener(X, psds)
         X = self.qtransform(X)
         return X, y
@@ -117,8 +117,8 @@ def build_val_batches(self, *args, **kwargs):
 
         return X_bg, X_inj
 
-    def augment(self, X):
-        X, y, psds = super().augment(X)
+    def inject(self, X, waveforms=None):
+        X, y, psds = super().inject(X, waveforms)
 
         # fft whiten and bandpass in frequency domain
         X = self.whiten(X, psds)
diff --git a/projects/train/train/data/waveforms/__init__.py b/projects/train/train/data/waveforms/__init__.py
new file mode 100644
index 000000000..9bc09b6db
--- /dev/null
+++ b/projects/train/train/data/waveforms/__init__.py
@@ -0,0 +1,2 @@
+from .loader import ChunkedWaveformDataset, Hdf5WaveformLoader, WaveformLoader
+from .sampler import WaveformSampler
diff --git a/projects/train/train/data/waveforms/generator/cbc.py b/projects/train/train/data/waveforms/generator/cbc.py
new file mode 100644
index 000000000..adb572752
--- /dev/null
+++ b/projects/train/train/data/waveforms/generator/cbc.py
@@ -0,0 +1,60 @@
+from typing import Callable
+
+import torch
+from ml4gw.waveforms.generator import TimeDomainCBCWaveformGenerator
+
+from .generator import WaveformGenerator
+
+
+class CBCGenerator(WaveformGenerator):
+    def __init__(
+        self,
+        *args,
+        approximant: Callable,
+        duration: float,
+        f_min: float,
+        f_ref: float,
+        **kwargs,
+    ):
+        """
+        A lightweight wrapper around
+        `ml4gw.waveforms.generator.TimeDomainCBCWaveformGenerator`
+        to make it compatible with
+        `aframe.train.train.data.waveforms.generator.WaveformGenerator`.
+        Args:
+            *args:
+                Positional arguments passed to
+                `aframe.train.train.data.waveforms.generator.WaveformGenerator`
+            approximant:
+                A callable that takes parameter tensors
+                and returns the cross and plus polarizations.
+                For example, `ml4gw.waveforms.IMRPhenomD()`
+            duration:
+                Duration of the waveform in seconds
+            f_min:
+                Lowest frequency at which waveform signal content
+                is generated
+            f_ref:
+                Reference frequency
+            **kwargs:
+                Keyword arguments passed to
+                `aframe.train.train.data.waveforms.generator.WaveformGenerator`
+        """
+        super().__init__(*args, **kwargs)
+
+        self.approximant = approximant
+        self.f_ref = f_ref
+        self.waveform_generator = TimeDomainCBCWaveformGenerator(
+            approximant,
+            self.sample_rate,
+            duration,
+            f_min,
+            f_ref,
+            self.right_pad,
+        )
+
+    def forward(self, **parameters) -> torch.Tensor:
+        hc, hp = self.waveform_generator(**parameters)
+        waveforms = torch.stack([hc, hp], dim=1)
+        hc, hp = waveforms.transpose(1, 0)
+        return hc.float(), hp.float()
diff --git a/projects/train/train/data/waveforms/generator/generator.py b/projects/train/train/data/waveforms/generator/generator.py
new file mode 100644
index 000000000..30cfaa5f6
--- /dev/null
+++ b/projects/train/train/data/waveforms/generator/generator.py
@@ -0,0 +1,43 @@
+from typing import TYPE_CHECKING
+
+from ....prior import AframePrior
+from ..sampler import WaveformSampler
+import torch
+
+
+if TYPE_CHECKING:
+    pass
+
+
+class WaveformGenerator(WaveformSampler):
+    def __init__(
+        self,
+        *args,
+        training_prior: AframePrior,
+        **kwargs,
+    ):
+        """
+        A torch module for generating waveforms on the fly.
+        Args:
+            training_prior:
+                A callable that takes an integer N and returns a
+                dictionary of parameter Tensors, each of length `N`
+        """
+        super().__init__(*args, **kwargs)
+        self.training_prior = training_prior
+
+    def get_train_waveforms(self, *_):
+        """
+        Method is not implemented for this class, as
+        waveforms are generated on the fly.
+        """
+        pass
+
+    def sample(self, X: torch.Tensor):
+        N = len(X)
+        parameters = self.training_prior(N, device=X.device)
+        hc, hp = self(**parameters)
+        return hc, hp
+
+    def forward(self):
+        raise NotImplementedError
diff --git a/projects/train/train/waveform_sampler.py b/projects/train/train/data/waveforms/loader.py
similarity index 84%
rename from projects/train/train/waveform_sampler.py
rename to projects/train/train/data/waveforms/loader.py
index 371847874..3538ccdaf 100644
--- a/projects/train/train/waveform_sampler.py
+++ b/projects/train/train/data/waveforms/loader.py
@@ -1,14 +1,53 @@
+from pathlib import Path
+
 import logging
 import math
 import warnings
-from pathlib import Path
 from typing import Iterable, Optional
 
 import h5py
 import numpy as np
 import torch
-from ml4gw.distributions import Cosine
-from torch.distributions.uniform import Uniform
+
+from ledger.injections import WaveformPolarizationSet
+from .sampler import WaveformSampler
+
+
+class WaveformLoader(WaveformSampler):
+    """
+    Module that should be used if training waveforms
+    are loaded from disk. The main function is to make
+    waveform handling consistent whether the waveforms
+    are generated during training or loaded from disk
+    The actual loading of the training waveforms is done
+    by `waveform_dataloader` in `train.data.base.train_dataloader()`.
+    """
+
+    def __init__(
+        self,
+        *args,
+        training_waveform_path: Path,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        if training_waveform_path.is_dir():
+            self.training_waveform_files = list(
+                training_waveform_path.iterdir()
+            )
+        else:
+            self.training_waveform_files = [training_waveform_path]
+
+        waveform_set = WaveformPolarizationSet.read(
+            self.training_waveform_files[0]
+        )
+        if waveform_set.right_pad != self.right_pad:
+            raise ValueError(
+                "Training waveform file does not have the same "
+                "right pad as validation waveform file"
+            )
+
+    def get_train_waveforms(self, world_size, rank, device):
+        pass
 
 
 # TODO: move to ml4gw
@@ -216,31 +255,3 @@ def __iter__(self):
             except StopIteration:
                 break
             num_waveforms, _, _ = chunk.shape
-
-
-class WaveformSampler(torch.nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-        self.dec = Cosine()
-        self.psi = Uniform(0, torch.pi)
-        self.phi = Uniform(-torch.pi, torch.pi)
-
-    def forward(self, X, prob, waveforms):
-        # determine batch size from X and prob
-        rvs = torch.rand(size=X.shape[:1], device=X.device)
-        mask = rvs < prob
-        N = mask.sum().item()
-
-        # sample sky parameters for each injections
-        dec = self.dec.sample((N,)).to(X.device)
-        psi = self.psi.sample((N,)).to(X.device)
-        phi = self.phi.sample((N,)).to(X.device)
-
-        # now sample the actual waveforms we want to inject
-        idx = torch.randperm(waveforms.shape[0])[:N]
-        waveforms = waveforms[idx].to(X.device).float()
-
-        cross, plus = waveforms[:, 0], waveforms[:, 1]
-        polarizations = {"cross": cross, "plus": plus}
-
-        return dec, psi, phi, polarizations, mask
diff --git a/projects/train/train/data/waveforms/sampler.py b/projects/train/train/data/waveforms/sampler.py
new file mode 100644
index 000000000..ea87be969
--- /dev/null
+++ b/projects/train/train/data/waveforms/sampler.py
@@ -0,0 +1,78 @@
+from pathlib import Path
+from typing import List
+
+import torch
+from utils import x_per_y
+
+from ledger.injections import WaveformSet, waveform_class_factory
+
+Distribution = torch.distributions.Distribution
+
+
+class WaveformSampler(torch.nn.Module):
+    """
+    Base object defining methods that waveform producing classes
+    should implement. Should not be instantiated on its own.
+    Args:
+        ifos:
+            List of interferometers that are being trained on.
+        sample_rate:
+            Sample rate in Hz of generated waveforms
+        val_waveform_file:
+            Path to the validation waveforms file.
+    """
+
+    def __init__(
+        self,
+        *args,
+        ifos: List[str],
+        sample_rate: float,
+        val_waveform_file: Path,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self.ifos = ifos
+        self.sample_rate = sample_rate
+        self.val_waveform_file = val_waveform_file
+
+        waveform_set = self.waveform_set_cls.read(val_waveform_file)
+        self.num_val_waveforms = len(waveform_set)
+        self.right_pad = waveform_set.right_pad
+
+    @property
+    def waveform_set_cls(self):
+        cls = waveform_class_factory(
+            self.ifos,
+            WaveformSet,
+            "WaveformSet",
+        )
+        return cls
+
+    def get_slice_bounds(self, total, world_size, rank) -> tuple[int, int]:
+        """
+        Determine waveform indices to load for this device
+        given our rank and world size
+        """
+        per_dev = x_per_y(abs(total), world_size)
+        start = rank * per_dev
+        stop = (rank + 1) * per_dev
+        return start, stop
+
+    # Assuming that we're going to be loading validation waveforms
+    # from disk for now, so this function can be defined here.
+    def get_val_waveforms(self, world_size, rank):
+        """
+        Returns validation waveforms for this device
+        """
+        start, stop = self.get_slice_bounds(
+            self.num_val_waveforms, world_size, rank
+        )
+        waveform_set = self.waveform_set_cls.read(self.val_waveform_file)
+        return torch.Tensor(waveform_set.waveforms[start:stop])
+
+    def get_test_waveforms(self):
+        raise NotImplementedError
+
+    def sample(self):
+        """Defines how to sample waveforms for training"""
+        raise NotImplementedError
diff --git a/projects/train/train/model/supervised.py b/projects/train/train/model/supervised.py
index b8d33324f..5fe42b241 100644
--- a/projects/train/train/model/supervised.py
+++ b/projects/train/train/model/supervised.py
@@ -33,12 +33,12 @@ def score(self, X, X_fft):
         return self(X, X_fft)
 
     def train_step(self, batch: tuple[Tensor, Tensor]) -> Tensor:
-        X, X_fft, y = batch
+        (X, X_fft), y = batch
         y_hat = self(X, X_fft)
         return torch.nn.functional.binary_cross_entropy_with_logits(y_hat, y)
 
     def validation_step(self, batch, _) -> None:
-        shift, X_bg, X_inj, X_bg_fft, X_inj_fft = batch
+        shift, (X_bg, X_bg_fft), (X_inj, X_inj_fft) = batch
 
         y_bg = self.score(X_bg, X_bg_fft)
 
diff --git a/projects/train/train/prior.py b/projects/train/train/prior.py
new file mode 100644
index 000000000..f4c18448f
--- /dev/null
+++ b/projects/train/train/prior.py
@@ -0,0 +1,110 @@
+from typing import Callable, Optional
+
+import torch
+
+
+class AframePrior:
+    def __init__(
+        self,
+        priors: dict[str, torch.distributions.Distribution],
+        conversion_function: Optional[Callable] = None,
+        constraint_function: Optional[Callable] = None,
+    ):
+        """
+        A class for sampling parameters from a prior distribution
+
+        Args:
+            priors:
+                A dictionary of parameter samplers that take an integer N
+                and return a tensor of shape (N, ...) representing
+                samples from the prior distribution
+            conversion_function:
+                A callable that takes a dictionary of sampled parameters
+                and returns a dictionary of waveform generation parameters
+            constraint_function:
+                A callable that takes a dictionary of sampled parameters,
+                discards any that don't satisfy the constraint, and returns
+                the remaining parameters
+        """
+        super().__init__()
+        self.priors = priors
+        self.conversion_function = conversion_function or (lambda x: x)
+        self.constraint_function = constraint_function or (lambda x: x)
+
+    def __call__(
+        self,
+        N: int,
+        device: str = "cpu",
+    ) -> dict[str, torch.Tensor]:
+        """
+        Generates random samples from the prior
+
+        Args:
+            N: Number of samples to generate
+            device: Device to place the samples
+        """
+        # sample parameters from prior
+        parameters = {
+            k: v.sample((N,)).to(device) for k, v in self.priors.items()
+        }
+        # perform any necessary conversions
+        # to from sampled parameters to
+        # waveform generation parameters
+        parameters = self.conversion_function(parameters)
+
+        # Discard any samples that don't meet the constraint
+        parameters = self.constraint_function(parameters)
+
+        keys = list(parameters.keys())
+        while len(parameters[keys[0]]) < N:
+            new_params = {
+                k: v.sample((N,)).to(device) for k, v in self.priors.items()
+            }
+            new_params = self.conversion_function(parameters)
+            new_params = self.constraint_function(parameters)
+            parameters = {
+                key: torch.cat([parameters[key], new_params[key]])
+                for key in keys
+            }
+
+        return {k: v[:N] for k, v in parameters.items()}
+
+    def log_prob(self, samples: dict[str, torch.Tensor]) -> torch.Tensor:
+        """
+        Calculate the log probability of samples under the prior.
+        TODO: This calculation is incorrect if a constraint function
+        is specified. We don't use this function anywhere currently,
+        so not too big a deal, but we'll need to fix this if we do.
+
+        Args:
+            samples:
+                Dictionary where key is parameter and
+                value is tensor of samples
+        """
+
+        first = samples[list(samples.keys())[0]]
+        log_probs = torch.ones(len(first), device=first.device)
+        for param, tensor in samples.items():
+            log_probs += self.priors[param].log_prob(tensor).to(first.device)
+        return log_probs
+
+
+class ParameterTransformer(torch.nn.Module):
+    """
+    Helper class for applying preprocessing
+    transformations to inference parameters
+    """
+
+    def __init__(self, **transforms: Callable):
+        super().__init__()
+        self.transforms = transforms
+
+    def forward(
+        self,
+        parameters: dict[str, torch.Tensor],
+    ):
+        # transform parameters
+        transformed = {k: v(parameters[k]) for k, v in self.transforms.items()}
+        # update parameter dict
+        parameters.update(transformed)
+        return parameters
diff --git a/projects/train/uv.lock b/projects/train/uv.lock
index 9b536d7e1..be5a7d7b8 100644
--- a/projects/train/uv.lock
+++ b/projects/train/uv.lock
@@ -38,9 +38,9 @@ requires-dist = [
     { name = "cloudpathlib", specifier = ">=0.18.1,<0.19" },
     { name = "jsonargparse", specifier = ">=4.27.1,<5" },
     { name = "kr8s", specifier = ">=0.10.0,<0.11" },
-    { name = "law", specifier = ">=0.1.19" },
+    { name = "law", specifier = ">=0.1.20" },
     { name = "luigi", specifier = "~=3.0" },
-    { name = "ml4gw-hermes", specifier = ">=0.2.0" },
+    { name = "ml4gw-hermes", specifier = ">=0.2.1" },
     { name = "numpy", specifier = ">=1.26.4,<2" },
     { name = "psutil", specifier = ">=5.9.8,<6" },
     { name = "pykube-ng", extras = ["oidc"], specifier = ">=23.6.0,<24" },
@@ -220,6 +220,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/46/eb/e7f063ad1fec6b3178a3cd82d1a3c4de82cccf283fc42746168188e1cdd5/anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a", size = 96041 },
 ]
 
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 },
+]
+
 [[package]]
 name = "architectures"
 version = "0.1.0"
@@ -242,6 +251,52 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [{ name = "pytest", specifier = ">=8.2.1,<9" }]
 
+[[package]]
+name = "argon2-cffi"
+version = "25.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "argon2-cffi-bindings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657 },
+]
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cffi" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b9/e9/184b8ccce6683b0aa2fbb7ba5683ea4b9c5763f1356347f1312c32e3c66e/argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3", size = 1779911 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d4/13/838ce2620025e9666aa8f686431f67a29052241692a3dd1ae9d3692a89d3/argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367", size = 29658 },
+    { url = "https://files.pythonhosted.org/packages/b3/02/f7f7bb6b6af6031edb11037639c697b912e1dea2db94d436e681aea2f495/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d", size = 80583 },
+    { url = "https://files.pythonhosted.org/packages/ec/f7/378254e6dd7ae6f31fe40c8649eea7d4832a42243acaf0f1fff9083b2bed/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae", size = 86168 },
+    { url = "https://files.pythonhosted.org/packages/74/f6/4a34a37a98311ed73bb80efe422fed95f2ac25a4cacc5ae1d7ae6a144505/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:58ed19212051f49a523abb1dbe954337dc82d947fb6e5a0da60f7c8471a8476c", size = 82709 },
+    { url = "https://files.pythonhosted.org/packages/74/2b/73d767bfdaab25484f7e7901379d5f8793cccbb86c6e0cbc4c1b96f63896/argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:bd46088725ef7f58b5a1ef7ca06647ebaf0eb4baff7d1d0d177c6cc8744abd86", size = 83613 },
+    { url = "https://files.pythonhosted.org/packages/4f/fd/37f86deef67ff57c76f137a67181949c2d408077e2e3dd70c6c42912c9bf/argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_i686.whl", hash = "sha256:8cd69c07dd875537a824deec19f978e0f2078fdda07fd5c42ac29668dda5f40f", size = 84583 },
+    { url = "https://files.pythonhosted.org/packages/6f/52/5a60085a3dae8fded8327a4f564223029f5f54b0cb0455a31131b5363a01/argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:f1152ac548bd5b8bcecfb0b0371f082037e47128653df2e8ba6e914d384f3c3e", size = 88475 },
+    { url = "https://files.pythonhosted.org/packages/8b/95/143cd64feb24a15fa4b189a3e1e7efbaeeb00f39a51e99b26fc62fbacabd/argon2_cffi_bindings-21.2.0-cp36-abi3-win32.whl", hash = "sha256:603ca0aba86b1349b147cab91ae970c63118a0f30444d4bc80355937c950c082", size = 27698 },
+    { url = "https://files.pythonhosted.org/packages/37/2c/e34e47c7dee97ba6f01a6203e0383e15b60fb85d78ac9a15cd066f6fe28b/argon2_cffi_bindings-21.2.0-cp36-abi3-win_amd64.whl", hash = "sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f", size = 30817 },
+    { url = "https://files.pythonhosted.org/packages/5a/e4/bf8034d25edaa495da3c8a3405627d2e35758e44ff6eaa7948092646fdcc/argon2_cffi_bindings-21.2.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93", size = 53104 },
+]
+
+[[package]]
+name = "arrow"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "python-dateutil" },
+    { name = "types-python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2e/00/0f6e8fcdb23ea632c866620cc872729ff43ed91d284c866b515c6342b173/arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85", size = 131960 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f8/ed/e97229a566617f2ae958a6b13e7cc0f585470eac730a73e9e82c32a3cdd2/arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80", size = 66419 },
+]
+
 [[package]]
 name = "astropy"
 version = "6.1.7"
@@ -287,6 +342,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/00/14/28e7254183bb53502b323b7e170eaeb86501cc774e76f82ef04216699afb/astropy_iers_data-0.2025.2.24.0.34.4-py3-none-any.whl", hash = "sha256:be8b3b75b09b1aa1d22de9b5243854e00d19936aca6d8f64466d16197c04bb28", size = 1946502 },
 ]
 
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918 },
+]
+
+[[package]]
+name = "async-lru"
+version = "2.0.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b2/4d/71ec4d3939dc755264f680f6c2b4906423a304c3d18e96853f0a595dfe97/async_lru-2.0.5.tar.gz", hash = "sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb", size = 10380 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/03/49/d10027df9fce941cb8184e78a02857af36360d33e1721df81c5ed2179a1a/async_lru-2.0.5-py3-none-any.whl", hash = "sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943", size = 6069 },
+]
+
 [[package]]
 name = "async-timeout"
 version = "5.0.1"
@@ -305,6 +381,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fc/30/d4986a882011f9df997a55e6becd864812ccfcd821d64aac8570ee39f719/attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a", size = 63152 },
 ]
 
+[[package]]
+name = "babel"
+version = "2.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 },
+]
+
 [[package]]
 name = "bayesian-optimization"
 version = "1.5.1"
@@ -333,6 +418,66 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f9/49/6abb616eb3cbab6a7cca303dc02fdf3836de2e0b834bf966a7f5271a34d8/beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16", size = 186015 },
 ]
 
+[[package]]
+name = "bilby"
+version = "2.5.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "bilby-cython" },
+    { name = "corner" },
+    { name = "dill" },
+    { name = "dynesty" },
+    { name = "emcee" },
+    { name = "h5py" },
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "scipy" },
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+]
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -3354,6 +4341,7 @@ dependencies = [
     { name = "lightning" },
     { name = "lightray" },
     { name = "ml4gw" },
+    { name = "priors" },
     { name = "ray", extra = ["default", "tune"] },
     { name = "s3fs" },
     { name = "torch" },
@@ -3365,6 +4353,7 @@ dependencies = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "jupyter" },
     { name = "pytest" },
 ]
 
@@ -3381,7 +4370,8 @@ requires-dist = [
     { name = "ledger", editable = "../../libs/ledger" },
     { name = "lightning", specifier = "==2.2.1" },
     { name = "lightray", specifier = ">=0.2.3" },
-    { name = "ml4gw", specifier = ">=0.7.2" },
+    { name = "ml4gw", specifier = ">=0.7.7" },
+    { name = "priors", editable = "../../libs/priors" },
     { name = "ray", extras = ["default", "tune"], specifier = ">=2.8.0,<3" },
     { name = "s3fs", specifier = ">=2024,<2025" },
     { name = "torch", specifier = "==2.5.0" },
@@ -3392,7 +4382,19 @@ requires-dist = [
 ]
 
 [package.metadata.requires-dev]
-dev = [{ name = "pytest", specifier = "~=7.3" }]
+dev = [
+    { name = "jupyter", specifier = ">=1.0.0" },
+    { name = "pytest", specifier = "~=7.3" },
+]
+
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621 }
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+]
 
 [[package]]
 name = "triton"
@@ -3433,6 +4435,15 @@ all = [
     { name = "python-rapidjson" },
 ]
 
+[[package]]
+name = "types-python-dateutil"
+version = "2.9.0.20250708"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c9/95/6bdde7607da2e1e99ec1c1672a759d42f26644bbacf939916e086db34870/types_python_dateutil-2.9.0.20250708.tar.gz", hash = "sha256:ccdbd75dab2d6c9696c350579f34cffe2c281e4c5f27a585b2a2438dd1d5c8ab", size = 15834 }
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+]
+
 [[package]]
 name = "typeshed-client"
 version = "2.7.0"
@@ -3464,6 +4475,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0f/dd/84f10e23edd882c6f968c21c2434fe67bd4a528967067515feca9e611e5e/tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639", size = 346762 },
 ]
 
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140 },
+]
+
 [[package]]
 name = "urllib3"
 version = "1.26.20"
@@ -3549,6 +4569,51 @@ wheels = [
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+[[package]]
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+[[package]]
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+[[package]]
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 [[package]]
 name = "wrapt"
 version = "1.17.2"
diff --git a/scripts/aframe_init.py b/scripts/aframe_init.py
index cad8a9db6..9c692c025 100644
--- a/scripts/aframe_init.py
+++ b/scripts/aframe_init.py
@@ -21,6 +21,7 @@
     root / "aframe" / "pipelines" / "sandbox" / "configs" / "base.cfg",
     root / "projects" / "train" / "train.yaml",
     root / "projects" / "export" / "export.yaml",
+    root / "projects" / "train" / "configs" / "training_prior.yaml",
 ]
 
 REVIEW_CONFIGS = [