diff --git a/roll/pipeline/agentic/agentic_config.py b/roll/pipeline/agentic/agentic_config.py
index 231f3859d..7f07e8de4 100644
--- a/roll/pipeline/agentic/agentic_config.py
+++ b/roll/pipeline/agentic/agentic_config.py
@@ -200,6 +200,16 @@ class AgenticConfig(PPOConfig):
     open_feedback_turn: bool = field(default=False, metadata={"help": "open feedback turn"})
     use_token_reward: bool = field(default=False, metadata={"help": "use token reward"})
 
+    estimated_chunks_per_traj: int = field(
+        default=0,
+        metadata={
+            "help": "Estimated average number of training chunks (turns) per trajectory for step-level "
+                    "agentic training (e.g., AgentNativeStepEnvManager). When > 0, the LR scheduler "
+                    "budget is multiplied by this factor to prevent premature exhaustion. "
+                    "When 0 (default), auto-detected from custom_envs max_steps (conservative: max_steps // 2)."
+        },
+    )
+
     batch_adjust_mode: Literal["copy", "delete", "auto", "random_sample"] = field(
         default="copy", metadata={"help": "batch adjust mode: copy or delete"}
     )
@@ -357,6 +367,83 @@ def __post_init__(self):
         # Apply OPD configuration at the end (handles student_train/student_infer/teacher mapping)
         self._apply_opd_config()
 
+    def _get_chunks_per_traj_estimate(self) -> int:
+        """Estimate the average number of training chunks per trajectory.
+
+        For step-level env managers (e.g., AgentNativeStepEnvManager), each trajectory
+        produces multiple training samples (one per turn/step). This method estimates
+        the multiplier from custom_envs config or the explicit override field.
+
+        Returns:
+            int: estimated chunks per trajectory (minimum 1)
+        """
+        if self.estimated_chunks_per_traj > 0:
+            return self.estimated_chunks_per_traj
+
+        # Auto-detect from custom_envs: look for max_steps in env configs
+        max_env_steps = 0
+        if self.custom_envs:
+            for tag, cfg in self.custom_envs.items():
+                if hasattr(cfg, 'max_steps') and cfg.max_steps is not None:
+                    max_env_steps = max(max_env_steps, int(cfg.max_steps))
+                elif isinstance(cfg, dict) and 'max_steps' in cfg:
+                    max_env_steps = max(max_env_steps, int(cfg['max_steps']))
+
+        if max_env_steps > 1:
+            # Conservative estimate: half of max_steps as average turns per trajectory
+            estimate = max(1, max_env_steps // 2)
+            logger.info(
+                f"Auto-detected estimated_chunks_per_traj={estimate} "
+                f"(from custom_envs max_steps={max_env_steps}, using max_steps // 2)"
+            )
+            return estimate
+
+        return 1
+
+    def set_max_steps(self, max_steps: int):
+        """Override to account for multi-turn chunking in agentic training.
+
+        For step-level env managers (AgentNativeStepEnvManager), each trajectory
+        produces multiple training samples (chunks), one per agent turn. The parent
+        implementation only accounts for rollout_batch_size (trajectories), causing
+        the LR scheduler to exhaust early when chunks >> trajectories.
+
+        This override multiplies the optimizer step budget by an estimate of chunks
+        per trajectory to align the scheduler with actual training dynamics.
+        """
+        chunks_multiplier = self._get_chunks_per_traj_estimate()
+
+        actor_backward_batch_size = (
+            self.actor_train.training_args.per_device_train_batch_size
+            * self.actor_train.training_args.gradient_accumulation_steps
+        )
+        critic_backward_batch_size = (
+            self.critic.training_args.per_device_train_batch_size
+            * self.critic.training_args.gradient_accumulation_steps
+        )
+
+        self.actor_train.training_args.max_steps = max(1, (
+            max_steps
+            * self.rollout_batch_size
+            * self.actor_infer.generating_args.num_return_sequences
+            * self.ppo_epochs
+            * chunks_multiplier
+            // actor_backward_batch_size
+        ))
+        self.critic.training_args.max_steps = max(1, (
+            max_steps
+            * self.rollout_batch_size
+            * self.actor_infer.generating_args.num_return_sequences
+            * chunks_multiplier
+            // critic_backward_batch_size
+        ))
+
+        logger.info(f"pipeline max_steps: {self.max_steps} to {max_steps}")
+        logger.info(f"chunks_per_traj_multiplier: {chunks_multiplier}")
+        logger.info(f"actor train max_steps without dp_size: {self.actor_train.training_args.max_steps}")
+        logger.info(f"critic train max_steps without dp_size: {self.critic.training_args.max_steps}")
+        self.max_steps = max_steps
+
     def make_env_configs(self, env_manager_config: EnvManagerConfig):
         # construct env configs
         env_configs = defaultdict(defaultdict)