diff --git a/.gitignore b/.gitignore
index e603f5015..94d952295 100644
--- a/.gitignore
+++ b/.gitignore
@@ -171,3 +171,5 @@ applications/ColossalChat/*.txt
 applications/ColossalChat/*.db
 applications/ColossalChat/stdin
 applications/ColossalChat/*.zip
+applications/ColossalChat/*.prof
+applications/ColossalChat/*.png
diff --git a/applications/ColossalChat/coati/distributed/grpo_consumer.py b/applications/ColossalChat/coati/distributed/grpo_consumer.py
index 51cdcf322..040ca61a4 100644
--- a/applications/ColossalChat/coati/distributed/grpo_consumer.py
+++ b/applications/ColossalChat/coati/distributed/grpo_consumer.py
@@ -280,13 +280,21 @@ class GRPOConsumer(BaseConsumer):
                             )
 
                         if self.booster.plugin.stage_manager.is_last_stage():
-                            reference_model_logits = reference_model_outputs["outputs"]["logits"]
-                            reference_action_log_probs = calc_action_log_probs(
-                                reference_model_logits / self.generate_config["temperature"],
-                                input_ids_forward_micro_batch,
-                                num_action,
-                                self.plugin.shard_config,
+                            reference_action_log_probs = torch.zeros(
+                                (input_ids_forward_micro_batch.size(0), num_action),
+                                device=input_ids_forward_micro_batch.device,
                             )
+                            for i in range(reference_action_log_probs.size(0)):
+                                # activation for log_softmax is too large if vocab size and sequence length are large
+                                # e.g., when using 152064 vocab size with 32K seqence length and a micro batch size of 4 (for pp=4 for example),
+                                # this activation sorely takes 152064*32000*4*4/1024/1024/1024=72.5GB
+                                reference_action_log_probs[i, :] += calc_action_log_probs(
+                                    reference_model_outputs["outputs"]["logits"][i : i + 1]
+                                    / self.generate_config["temperature"],
+                                    input_ids_forward_micro_batch[i : i + 1],
+                                    num_action,
+                                    self.plugin.shard_config,
+                                )[0]
                         else:
                             # Dummy reference logprobs for data iterator.
                             reference_action_log_probs = None
@@ -308,12 +316,19 @@ class GRPOConsumer(BaseConsumer):
 
                     def _criterion(outputs, inputs):
                         action_logits = outputs.logits
-                        action_log_probs = calc_action_log_probs(
-                            action_logits / self.generate_config["temperature"],
-                            inputs["input_ids"],
-                            num_action,
-                            self.plugin.shard_config,
+                        action_log_probs = torch.zeros(
+                            (inputs["input_ids"].size(0), num_action), device=action_logits.device
                         )
+                        for i in range(action_log_probs.size(0)):
+                            # activation for log_softmax is too large if vocab size and sequence length are large
+                            # e.g., when using 152064 vocab size with 32K seqence length and a micro batch size of 4 (for pp=4 for example),
+                            # this activation sorely takes 152064*32000*4*4/1024/1024/1024=72.5GB
+                            action_log_probs[i, :] += calc_action_log_probs(
+                                action_logits[i : i + 1] / self.generate_config["temperature"],
+                                inputs["input_ids"][i : i + 1],
+                                num_action,
+                                self.plugin.shard_config,
+                            )[0]
                         if "reference_action_log_probs" in inputs:
                             per_token_kl = (
                                 torch.exp(inputs["reference_action_log_probs"] - action_log_probs)