Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification

2026-09-07

AI Summary

本文针对强化学习训练中的不稳定性和熵崩溃问题，提出了一种基于数据动态筛选和分层重放机制的方法来稳定训练过程。

Abstract

While Reinforcement Learning (RL) effectively incentivizes reasoning in Large Language Models, current pipelines are hindered by training instability and rapid entropy collapse. These limitations often stem from"Rollout Silencing"and low-quality gradient signals in standard sampling procedures. In this work, we propose a robust, data-centric framework to stabilize RL training. We first introduce Potential-Aware Query Mining (PAQM), which filters data dynamically to focus on the"Distillation Zone"---samples with high potential for capability elicitation. Furthermore, we present Hybrid Stratified Replay (HSR), a novel mechanism that restructures batches by stratifying rollouts based on Path Entropy, a rollout-level confidence proxy, and outcome reward. Within each optimization step, HSR reuses current-policy"Stability Anchors"and"Hard Negatives"to construct high-contrast optimization groups, then clears its buffers before the next step. This approach mitigates entropy collapse while improving the utilization of learning signals under limited compute. Our method outperforms strong baselines on complex reasoning tasks, offering a principled solution for stable and efficient RL fine-tuning.

Problem

• Reinforcement Learning
• Training Instability
• Entropy Collapse
• Rollout Silencing
• Gradient Signals

Innovation

• Potential-Aware Query Mining
• Hybrid Stratified Replay
• Path Entropy