English(EN) Aligning Training and Rollout Policies for End-to-End FP4 RL on Mixture-of-Experts LLMs

新的MoEResQ方法改进了混合专家LLM的FP4强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了MoEResQ，一种将FP4强化学习（RL）应用于混合专家（MoE）大型语言模型的新方法。与适用于密集模型的先前方法不同，MoE架构独特的路由机制要求在训练和推理过程中对称地应用恢复技术以保持准确性。MoEResQ强制执行这种对称性，显著提高了训练-推理对齐度，并缩小了与全精度模型的准确性差距。 AI

影响确立了将低精度训练应用于MoE模型的一个关键原则，可能能够实现更高效的LLM开发。

排序理由详细介绍LLM训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Hei Yi Mak, Hoang Le, Mehran Taghian Jazi, Yunke Peng, Huan Lin, Zhengzhi Tang, Zhexu Liu, Yao Wang, Yaoyuan Wang, Anandharaju Durai Raju, Tanzila Rahman · 2026-11-05 05:00

面向混合专家LLM的端到端FP4强化学习的训练与部署策略对齐

arXiv:2611.04376v2 Announce Type: replace-cross Abstract: In this work, we study end-to-end FP4 reinforcement learning (RL) post-training for Mixture-of-Experts (MoE) large language models. Recent work on FP4 RL has introduced effective recovery recipes that restore accuracy unde…

本文如何被排名

报道来源 [1]

面向混合专家LLM的端到端FP4强化学习的训练与部署策略对齐

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