diff --git a/torchrl/data/tensor_specs.py b/torchrl/data/tensor_specs.py
index 67cbda48f14..aec148f9523 100644
--- a/torchrl/data/tensor_specs.py
+++ b/torchrl/data/tensor_specs.py
@@ -4941,7 +4941,7 @@ def set(self, name: str, spec: TensorSpec) -> Composite:
                     spec.shape = self.shape
                 else:
                     raise ValueError(
-                        f"The shape of the spec {type(spec).__name__} and the Composite {type(self).__name__} mismatch: the first "
+                        f"The shapes of the spec {type(spec).__name__} and the {type(self).__name__} mismatch: the first "
                         f"{self.ndim} dimensions should match but got spec.shape={spec.shape} and "
                         f"Composite.shape={self.shape}."
                     )
diff --git a/torchrl/envs/transforms/llm.py b/torchrl/envs/transforms/llm.py
index d4f38843ba1..1fb7666d24d 100644
--- a/torchrl/envs/transforms/llm.py
+++ b/torchrl/envs/transforms/llm.py
@@ -6,18 +6,20 @@
 
 from collections import deque
 from collections.abc import Mapping
-from copy import copy, deepcopy
+from copy import copy
 from typing import Any, Callable, Iterable, Literal
 
 import torch
 from tensordict import lazy_stack, NestedKey, TensorDict, TensorDictBase, unravel_key
-from tensordict.nn import ProbabilisticTensorDictModule, TensorDictParams
+from tensordict.nn import (
+    ProbabilisticTensorDictModule,
+    ProbabilisticTensorDictSequential,
+)
 from tensordict.utils import _zip_strict, is_seq_of_nested_key
-from torch import nn
 
 from torchrl.data.tensor_specs import Composite, NonTensor, TensorSpec, Unbounded
 from torchrl.envs.transforms.transforms import TensorDictPrimer, Transform
-from torchrl.envs.transforms.utils import _set_missing_tolerance, _stateless_param
+from torchrl.envs.transforms.utils import _set_missing_tolerance
 from torchrl.envs.utils import make_composite_from_td
 
 
@@ -500,6 +502,10 @@ def _load_from_dataloader(self, reset: torch.Tensor | None = None):
             return self._queue.popleft()
         return out
 
+    def __repr__(self) -> str:
+        class_name = self.__class__.__name__
+        return f"{class_name}(primers={self.primers}, dataloader={self.dataloader})"
+
 
 class KLRewardTransform(Transform):
     """A transform to add a KL[pi_current||pi_0] correction term to the reward.
diff --git a/torchrl/modules/llm/transformers_policy.py b/torchrl/modules/llm/transformers_policy.py
index c506481746e..e977fc18be6 100644
--- a/torchrl/modules/llm/transformers_policy.py
+++ b/torchrl/modules/llm/transformers_policy.py
@@ -53,11 +53,11 @@ def log_probs_from_scores(td: TensorDictBase) -> TensorDictBase:
     - "tokens_out", "scores"
 
     """
-    # TODO: how do we avoid getting these?
     tokens_out = td["tokens_out", "sequences"]
     seq_len = tokens_out.shape[1]
 
     del td["tokens_out", "past_key_values"]
+
     scores = dict(td["tokens_out", "scores"].items())
     scores = torch.stack(
         [scores[str(k)] for k in range(len(scores))], 1
@@ -90,15 +90,18 @@ def log_probs_from_logits(td: TensorDictBase) -> TensorDictBase:
     - "forward", "past_key_values"
     - "forward"
     """
-    # TODO: how do we avoid getting these?
+    tokens_out = td["tokens_response", "input_ids"]
+    seq_len = tokens_out.shape[-1]
+
     del td["forward", "past_key_values"]
+
     scores = td["forward", "logits"]
+    scores = scores[..., -seq_len:, :]
     logits = scores - scores.logsumexp(dim=-1, keepdim=True)
     td["logits"] = scores
     del td["forward"]
     scores.shape[1]
-    tokens = td["tokens_in", "input_ids"]
-    log_probs = logits.gather(-1, tokens.unsqueeze(-1))
+    log_probs = logits.gather(-1, tokens_out.unsqueeze(-1))
     td["log_probs"] = log_probs
     return td