@inproceedings{ye-etal-2026-stable,
title = "Stable-{MM}-{R}1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification",
author = "Ye, Yimeng and
Chen, Shuang and
Huang, Wenxuan and
Zhang, Manyuan and
Feng, Kaituo and
Chen, Zhangquan and
Chen, Jiayu and
Zhou, Yucheng and
Xiao, YiCheng and
Feng, ZhiYuan and
Shi, Tianyu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.90/",
pages = "1840--1853",
ISBN = "979-8-89176-462-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification
%A Ye, Yimeng
%A Chen, Shuang
%A Huang, Wenxuan
%A Zhang, Manyuan
%A Feng, Kaituo
%A Chen, Zhangquan
%A Chen, Jiayu
%A Zhou, Yucheng
%A Xiao, YiCheng
%A Feng, ZhiYuan
%A Shi, Tianyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ye-etal-2026-stable
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.90/
%P 1840-1853
Markdown (Informal)
[Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification](https://aclanthology.org/2026.findings-emnlp.90/) (Ye et al., Findings 2026)
ACL
- Yimeng Ye, Shuang Chen, Wenxuan Huang, Manyuan Zhang, Kaituo Feng, Zhangquan Chen, Jiayu Chen, Yucheng Zhou, YiCheng Xiao, ZhiYuan Feng, and Tianyu Shi. 2026. Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 1840–1853, Budapest, Hungary. Association for Computational Linguistics.