diff --git a/nemo_reinforcer/algorithms/loss_functions.py b/nemo_reinforcer/algorithms/loss_functions.py
index 6a5d6b593d..158c9824eb 100644
--- a/nemo_reinforcer/algorithms/loss_functions.py
+++ b/nemo_reinforcer/algorithms/loss_functions.py
@@ -58,7 +58,7 @@ class ClippedPGLossFn(LossFunction):
     - r_t(θ) = π_θ(a_t|s_t) / π_θ_old(a_t|s_t) is the probability ratio
     - A_t is the advantage estimate
     - ε is the clip parameter (ratio_eps)
-        - As proposed in the DAPO paper (https://arxiv.org/pdf/2503.14476), 
+        - As proposed in the DAPO paper (https://arxiv.org/pdf/2503.14476),
           we allow setting a distinct minimum and maximum value for the clip parameter (set to the same value for PPO/GRPO/etc.)
             - ratio_eps_min: minimum value for the clip parameter
             - ratio_eps_max: maximum value for the clip parameter
diff --git a/nemo_reinforcer/data/llm_message_utils.py b/nemo_reinforcer/data/llm_message_utils.py
index 5ae8bee9a8..f6cb3c8079 100644
--- a/nemo_reinforcer/data/llm_message_utils.py
+++ b/nemo_reinforcer/data/llm_message_utils.py
@@ -353,14 +353,13 @@ def get_formatted_message_log(
     Returns:
         The message log with updated 'token_ids' and 'content' fields.
     """
-    cu_message = []
+    new_message_log = []
     prev_formatted_message = ""
     template = task_data_spec.custom_template
 
     for i, message in enumerate(message_log):
-        cu_message.append(message.copy())
         formatted_message = tokenizer.apply_chat_template(
-            cu_message,
+            message_log[: i + 1],
             chat_template=template,
             add_generation_prompt=False,
             tokenize=False,
@@ -383,13 +382,17 @@ def get_formatted_message_log(
             message_chunk = message_chunk.rstrip("\n")
             if not message_chunk.endswith(tokenizer.eos_token):
                 message_chunk += tokenizer.eos_token
-        message["token_ids"] = tokenizer(
+
+        new_message = message.copy()
+        new_message["token_ids"] = tokenizer(
             message_chunk, return_tensors="pt", add_special_tokens=False
         )["input_ids"][0]
-        message["content"] = message_chunk
+        new_message["content"] = message_chunk
+        new_message_log.append(new_message)
+
         prev_formatted_message = formatted_message
 
-    return message_log
+    return new_message_log
 
 
 def remap_dataset_keys(