English(EN) Fed-GRPO: Reward-Signal-Driven Federated Group Relative Policy Optimization

Fed-GRPO 实现隐私保护的联邦大语言模型训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员推出了一种新颖的联邦学习框架 Fed-GRPO，用于在不损害数据隐私的情况下，利用强化学习训练大语言模型（LLMs）。该方法利用训练过程中生成的奖励信号作为通信高效信号，来指导聚合和本地训练过程。实验表明，Fed-GRPO 显著降低了通信开销，并在数学推理任务上接近集中式训练的性能。 AI

影响这项研究可能实现更具隐私保护和通信效率的大语言模型训练，以适应特定任务。

排序理由该集群包含一篇详细介绍大语言模型新训练方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Pengxin Guo, Shuang Zeng, Zonggen Li, Weiying Zheng, Mengting Liu, Liangqiong Qu · 2026-10-09 04:00

Fed-GRPO：由奖励信号驱动的联邦群体相对策略优化

arXiv:2610.11502v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL), particularly through Group Relative Policy Optimization (GRPO). However, existing GRPO methods assume centralize…