arXiv Artificial Intelligence

Fed-GRPO: Reward-Signal-Driven Federated Group Relative Policy Optimization

1 hour ago 09.10.2026 07:00 👁 1 views
Fed-GRPO: Reward-Signal-Driven Federated Group Relative Policy Optimization

Quick summary

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 centralized access to training data, which may not hold in practice due to privacy or regulatory constraints. To this end, we propose Fed-GRPO, a federated GRPO training framework that addresses these privacy constraints by enabling collaborative reasoning training without sharing raw data, which leverages the reward statistics

Key takeaways

  • 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 centralized access to training data, which may not hold in practice due to privacy or regulatory constraints.
  • To this end, we propose Fed-GRPO, a federated GRPO training framework that addresses these privacy constraints by enabling collaborative reasoning training without sharing raw data, which leverages the reward statistics

Why it matters

“Fed-GRPO: Reward-Signal-Driven Federated Group Relative Policy Optimization” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗