diff --git a/fastvideo/training/rl/stat_tracking.py b/fastvideo/training/rl/stat_tracking.py
new file mode 100644
index 000000000..59d48d0c1
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+++ b/fastvideo/training/rl/stat_tracking.py
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+# SPDX-License-Identifier: Apache-2.0
+"""
+Per-prompt statistics tracking for GRPO training.
+
+This module ports the PerPromptStatTracker from FlowGRPO to FastVideo.
+It tracks reward statistics per unique prompt and computes normalized advantages.
+
+Ported from:
+- flow_grpo/flow_grpo/stat_tracking.py
+
+Key adaptations:
+1. Uses FastVideo's logging instead of print statements
+2. Works with single GPU (no distributed logic)
+3. Supports numpy arrays and torch tensors
+"""
+
+import numpy as np
+from typing import Union
+
+import torch
+
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class PerPromptStatTracker:
+    """
+    Tracks reward statistics per unique prompt for advantage normalization.
+    
+    This class maintains running statistics (mean, std) for each unique prompt
+    and computes normalized advantages using either per-prompt or global statistics.
+    
+    Used in GRPO training to normalize advantages within groups of samples
+    generated from the same prompt, which helps stabilize training when different
+    prompts have different reward scales.
+    """
+
+    def __init__(self, global_std: bool = False):
+        """
+        Initialize the per-prompt stat tracker.
+        
+        Args:
+            global_std: If True, use global std across all rewards for normalization.
+                       If False, use per-prompt std (default, recommended for GRPO).
+        """
+        self.global_std = global_std
+        self.stats: dict[str, list] = {}  # Maps prompt -> list of rewards
+        self.history_prompts: set[int] = set()  # Set of hashed prompts seen
+
+    def update(
+        self,
+        prompts: Union[list[str], np.ndarray],
+        rewards: Union[list[float], np.ndarray, torch.Tensor],
+        type: str = 'grpo'
+    ) -> np.ndarray:
+        """
+        Update statistics and compute normalized advantages.
+        
+        Args:
+            prompts: List or array of prompt strings (one per sample)
+            rewards: Array or tensor of reward values (one per sample)
+            type: Advantage computation type:
+                - 'grpo': Normalize by (reward - mean) / std (default)
+                - 'rwr': Return rewards as-is (reward-weighted regression)
+                - 'sft': Binary advantages (1 for max, 0 otherwise)
+                - 'dpo': DPO-style advantages (1 for max, -1 for min)
+        
+        Returns:
+            advantages: Normalized advantages array [num_samples] or [num_samples, ...]
+                       Shape matches rewards shape
+        """
+        # Convert to numpy arrays
+        prompts = np.array(prompts)
+        if isinstance(rewards, torch.Tensor):
+            rewards = rewards.detach().cpu().numpy()
+        rewards = np.array(rewards, dtype=np.float64)
+        
+        # Ensure rewards are 1D (one reward per sample)
+        # FlowGRPO expects rewards to be aggregated per sample
+        if rewards.ndim > 1:
+            # If multi-dimensional, flatten or take mean
+            # For [B, num_steps] shape, we typically want one reward per sample
+            # So we take the mean across timesteps
+            if rewards.ndim == 2:
+                # Assume shape is [B, num_steps] - take mean across timesteps
+                rewards = rewards.mean(axis=1)
+            else:
+                # Flatten and take mean for higher dimensions
+                rewards = rewards.reshape(len(prompts), -1).mean(axis=1)
+        
+        # Ensure prompts and rewards have matching lengths
+        assert len(prompts) == len(rewards), \
+            f"Prompts ({len(prompts)}) and rewards ({len(rewards)}) must have same length"
+        
+        unique_prompts = np.unique(prompts)
+        advantages = np.zeros_like(rewards, dtype=np.float64)
+        
+        # First pass: collect rewards for each prompt
+        for prompt in unique_prompts:
+            prompt_mask = prompts == prompt
+            prompt_rewards = rewards[prompt_mask]
+            
+            # Store rewards in stats
+            if prompt not in self.stats:
+                self.stats[prompt] = []
+            self.stats[prompt].extend(prompt_rewards.tolist())
+            self.history_prompts.add(hash(prompt))
+        
+        # Second pass: compute statistics and advantages
+        for prompt in unique_prompts:
+            prompt_mask = prompts == prompt
+            prompt_rewards = rewards[prompt_mask]
+            
+            # Stack all historical rewards for this prompt
+            if len(self.stats[prompt]) > 0:
+                all_prompt_rewards = np.array(self.stats[prompt])
+            else:
+                all_prompt_rewards = prompt_rewards
+            
+            # Compute mean and std
+            mean = np.mean(all_prompt_rewards, axis=0, keepdims=True)
+            
+            if self.global_std:
+                # Use global std across all rewards
+                std = np.std(rewards, axis=0, keepdims=True) + 1e-4
+            else:
+                # Use per-prompt std
+                std = np.std(all_prompt_rewards, axis=0, keepdims=True) + 1e-4
+            
+            # Compute advantages based on type
+            if type == 'grpo':
+                # GRPO: normalize by (reward - mean) / std
+                advantages[prompt_mask] = (prompt_rewards - mean) / std
+            elif type == 'rwr':
+                # Reward-weighted regression: use rewards as-is
+                advantages[prompt_mask] = prompt_rewards
+            elif type == 'sft':
+                # Supervised fine-tuning: binary (1 for max, 0 otherwise)
+                max_reward = np.max(prompt_rewards)
+                advantages[prompt_mask] = (prompt_rewards == max_reward).astype(np.float64)
+            elif type == 'dpo':
+                # DPO-style: 1 for max, -1 for min
+                prompt_rewards_tensor = torch.tensor(prompt_rewards)
+                max_idx = torch.argmax(prompt_rewards_tensor)
+                min_idx = torch.argmin(prompt_rewards_tensor)
+                
+                # If all rewards are the same, use first two indices
+                if max_idx == min_idx:
+                    min_idx = torch.tensor(0)
+                    max_idx = torch.tensor(1) if len(prompt_rewards_tensor) > 1 else torch.tensor(0)
+                
+                result = torch.zeros_like(prompt_rewards_tensor, dtype=torch.float64)
+                result[max_idx] = 1.0
+                result[min_idx] = -1.0
+                advantages[prompt_mask] = result.numpy()
+            else:
+                raise ValueError(f"Unknown advantage type: {type}. Must be one of: 'grpo', 'rwr', 'sft', 'dpo'")
+        
+        return advantages
+
+    def get_stats(self) -> tuple[float, int]:
+        """
+        Get statistics about tracked prompts.
+        
+        Returns:
+            avg_group_size: Average number of samples per unique prompt
+            history_prompts: Number of unique prompts seen (across all updates)
+        """
+        if not self.stats:
+            avg_group_size = 0.0
+        else:
+            total_samples = sum(len(v) for v in self.stats.values())
+            avg_group_size = total_samples / len(self.stats)
+        
+        history_prompts = len(self.history_prompts)
+        
+        return avg_group_size, history_prompts
+
+    def clear(self) -> None:
+        """
+        Clear all statistics (but keep history_prompts for tracking).
+        
+        This is typically called after each epoch to reset per-epoch statistics
+        while maintaining a record of all prompts seen during training.
+        """
+        self.stats = {}
+        logger.debug("Cleared per-prompt statistics (kept %d unique prompts in history)", 
+                    len(self.history_prompts))