diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py
index 0d5443b22b4..afd8ae61765 100644
--- a/torchrl/collectors/collectors.py
+++ b/torchrl/collectors/collectors.py
@@ -398,6 +398,7 @@ class SyncDataCollector(DataCollectorBase):
             policy is ignored before it is called. This feature is mainly
             intended to be used in offline/model-based settings, where a
             batch of random trajectories can be used to initialize training.
+            If provided, it will be rounded up to the closest multiple of frames_per_batch.
             Defaults to ``None`` (i.e. no random frames).
         reset_at_each_iter (bool, optional): Whether environments should be reset
             at the beginning of a batch collection.
@@ -599,13 +600,26 @@ def __init__(
         self.total_frames = total_frames
         self.reset_at_each_iter = reset_at_each_iter
         self.init_random_frames = init_random_frames
+        if (
+            init_random_frames is not None
+            and init_random_frames % frames_per_batch != 0
+            and RL_WARNINGS
+        ):
+            warnings.warn(
+                f"init_random_frames ({init_random_frames}) is not exactly a multiple of frames_per_batch ({frames_per_batch}), "
+                f" this results in more init_random_frames than requested"
+                f" ({-(-init_random_frames // frames_per_batch) * frames_per_batch})."
+                "To silence this message, set the environment variable RL_WARNINGS to False."
+            )
+
         self.postproc = postproc
         if self.postproc is not None and hasattr(self.postproc, "to"):
             self.postproc.to(self.storing_device)
         if frames_per_batch % self.n_env != 0 and RL_WARNINGS:
             warnings.warn(
-                f"frames_per_batch {frames_per_batch} is not exactly divisible by the number of batched environments {self.n_env}, "
-                f" this results in more frames_per_batch per iteration that requested."
+                f"frames_per_batch ({frames_per_batch}) is not exactly divisible by the number of batched environments ({self.n_env}), "
+                f" this results in more frames_per_batch per iteration that requested"
+                f" ({-(-frames_per_batch // self.n_env) * self.n_env})."
                 "To silence this message, set the environment variable RL_WARNINGS to False."
             )
         self.requested_frames_per_batch = frames_per_batch
@@ -1026,6 +1040,7 @@ class _MultiDataCollector(DataCollectorBase):
             policy is ignored before it is called. This feature is mainly
             intended to be used in offline/model-based settings, where a
             batch of random trajectories can be used to initialize training.
+            If provided, it will be rounded up to the closest multiple of frames_per_batch.
             Defaults to ``None`` (i.e. no random frames).
         reset_at_each_iter (bool, optional): Whether environments should be reset
             at the beginning of a batch collection.