diff --git a/verl/workers/actor/dp_actor.py b/verl/workers/actor/dp_actor.py
index d0a694cfed9..f245587e715 100644
--- a/verl/workers/actor/dp_actor.py
+++ b/verl/workers/actor/dp_actor.py
@@ -414,6 +414,8 @@ def update_policy(self, data: DataProto):
                 self.actor_optimizer.zero_grad()
 
                 for data in micro_batches:
+                    micro_batch_metrics = {}
+
                     # Support all hardwares
                     if isinstance(data, DataProto):
                         data = {**data.batch.to(get_device_id()), **data.non_tensor_batch}
@@ -484,8 +486,8 @@ def update_policy(self, data: DataProto):
                         kl_loss = agg_loss(loss_mat=kld, loss_mask=response_mask, loss_agg_mode=loss_agg_mode)
 
                         policy_loss = policy_loss + kl_loss * self.config.kl_loss_coef
-                        metrics["actor/kl_loss"] = kl_loss.detach().item()
-                        metrics["actor/kl_coef"] = self.config.kl_loss_coef
+                        micro_batch_metrics["actor/kl_loss"] = kl_loss.detach().item()
+                        micro_batch_metrics["actor/kl_coef"] = self.config.kl_loss_coef
 
                     if self.config.use_dynamic_bsz:
                         # relative to the dynamic bsz
@@ -494,16 +496,18 @@ def update_policy(self, data: DataProto):
                         loss = policy_loss / self.gradient_accumulation
                     loss.backward()
 
-                    data = {
-                        "actor/pg_loss": pg_loss.detach().item(),
-                        "actor/pg_clipfrac": pg_clipfrac.detach().item(),
-                        "actor/ppo_kl": ppo_kl.detach().item(),
-                        "actor/pg_clipfrac_lower": pg_clipfrac_lower.detach().item(),
-                    }
-                    append_to_dict(metrics, data)
+                    micro_batch_metrics.update(
+                        {
+                            "actor/pg_loss": pg_loss.detach().item(),
+                            "actor/pg_clipfrac": pg_clipfrac.detach().item(),
+                            "actor/ppo_kl": ppo_kl.detach().item(),
+                            "actor/pg_clipfrac_lower": pg_clipfrac_lower.detach().item(),
+                        }
+                    )
+                    append_to_dict(metrics, micro_batch_metrics)
 
                 grad_norm = self._optimizer_step()
-                data = {"actor/grad_norm": grad_norm.detach().item()}
-                append_to_dict(metrics, data)
+                mini_batch_metrics = {"actor/grad_norm": grad_norm.detach().item()}
+                append_to_dict(metrics, mini_batch_metrics)
         self.actor_optimizer.zero_grad()
         return metrics
diff --git a/verl/workers/critic/dp_critic.py b/verl/workers/critic/dp_critic.py
index 9a6991db294..3a866928dcc 100644
--- a/verl/workers/critic/dp_critic.py
+++ b/verl/workers/critic/dp_critic.py
@@ -221,6 +221,8 @@ def update_critic(self, data: DataProto):
                 self.critic_optimizer.zero_grad()
 
                 for data in micro_batches:
+                    micro_batch_metrics = {}
+
                     # Support all devices
                     if isinstance(data, DataProto):
                         data = {**data.batch.to(get_device_id()), **data.non_tensor_batch}
@@ -254,16 +256,18 @@ def update_critic(self, data: DataProto):
 
                     loss.backward()
 
-                    data = {
-                        "critic/vf_loss": vf_loss.detach().item(),
-                        "critic/vf_clipfrac": vf_clipfrac.detach().item(),
-                        "critic/vpred_mean": masked_mean(vpreds, response_mask).detach().item(),
-                    }
+                    micro_batch_metrics.update(
+                        {
+                            "critic/vf_loss": vf_loss.detach().item(),
+                            "critic/vf_clipfrac": vf_clipfrac.detach().item(),
+                            "critic/vpred_mean": masked_mean(vpreds, response_mask).detach().item(),
+                        }
+                    )
 
-                    append_to_dict(metrics, data)
+                    append_to_dict(metrics, micro_batch_metrics)
 
                 grad_norm = self._optimizer_step()
-                data = {"critic/grad_norm": grad_norm.detach().item()}
-                append_to_dict(metrics, data)
+                mini_batch_metrics = {"critic/grad_norm": grad_norm.detach().item()}
+                append_to_dict(metrics, mini_batch_metrics)
         self.critic_optimizer.zero_grad()
         return metrics
diff --git a/verl/workers/fsdp_workers.py b/verl/workers/fsdp_workers.py
index d46b3d02605..dcf964ec202 100644
--- a/verl/workers/fsdp_workers.py
+++ b/verl/workers/fsdp_workers.py
@@ -1123,9 +1123,9 @@ def update_critic(self, data: DataProto):
             estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time)
             metrics["perf/mfu/critic"] = estimated_flops * self.config.ppo_epochs / promised_flops / self.world_size
 
-            self.critic_lr_scheduler.step()
             lr = self.critic_lr_scheduler.get_last_lr()[0]
             metrics["critic/lr"] = lr
+            self.critic_lr_scheduler.step()
 
             output = DataProto(batch=None, meta_info={"metrics": metrics})
             output = self.ulysses_sharding_manager.postprocess_data(data=output)