diff --git a/nemo_rl/data/llm_message_utils.py b/nemo_rl/data/llm_message_utils.py
index f2d24fc421..51cd5a279d 100644
--- a/nemo_rl/data/llm_message_utils.py
+++ b/nemo_rl/data/llm_message_utils.py
@@ -421,6 +421,12 @@ def get_formatted_message_log(
         new_message["token_ids"] = tokenizer(
             message_chunk, return_tensors="pt", add_special_tokens=False
         )["input_ids"][0]
+        if len(new_message["token_ids"]) == 0:
+            # if there is an empty message, the empty `token_ids` tensor ends up being in fp32,
+            # which causes `_validate_tensor_consistency` to fail. To fix this, we convert the
+            # empty tensor to int64.
+            new_message["token_ids"] = new_message["token_ids"].to(torch.int64)
+
         new_message["content"] = message_chunk
         new_message_log.append(new_message)
 
diff --git a/tests/unit/data/test_llm_message_utils.py b/tests/unit/data/test_llm_message_utils.py
index 0a5cb3ef4b..fc4c6c6b8d 100644
--- a/tests/unit/data/test_llm_message_utils.py
+++ b/tests/unit/data/test_llm_message_utils.py
@@ -18,8 +18,10 @@
 import torch
 from transformers import AutoTokenizer
 
+from nemo_rl.data.hf_datasets import COMMON_CHAT_TEMPLATES
 from nemo_rl.data.interfaces import LLMMessageLogType, TaskDataSpec
 from nemo_rl.data.llm_message_utils import (
+    _validate_tensor_consistency,
     add_loss_mask_to_message_log,
     batched_message_log_to_flat_message,
     get_first_index_that_differs,
@@ -406,6 +408,39 @@ def test_get_formatted_message_log_qwen(
     assert actual_text == expected_text
 
 
+def test_formatted_message_log_empty_message():
+    message_logs = [
+        [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": ""},
+        ],
+        [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello!"},
+        ],
+    ]
+    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+    tokenizer.chat_template = COMMON_CHAT_TEMPLATES.passthrough_prompt_response
+    task_data_spec = TaskDataSpec(task_name="test")
+    result = [
+        get_formatted_message_log(
+            message_log,
+            tokenizer,
+            task_data_spec,
+            add_bos_token=False,
+            add_eos_token=False,
+        )
+        for message_log in message_logs
+    ]
+    flat_result = [message_log_to_flat_messages(m) for m in result]
+    for k in flat_result[0].keys():
+        if isinstance(flat_result[0][k], torch.Tensor):
+            # make sure validate_tensor_consistency does not raise an error when one of the messages is empty
+            _validate_tensor_consistency(
+                [flat_result[i][k] for i in range(len(flat_result))]
+            )
+
+
 def test_add_loss_mask_to_chat_message_log(
     tokenized_chat_message_log: LLMMessageLogType,
 ):