The Joy of Many Minds: How JoyAgents-R1 Unleashes the Power of Multi-LLM Reinforcement Learning
When it comes to language model agents, more minds may not always mean merrier results. Multi-agent reinforcement learning (MARL) promises a flexible path for decomposing and solving complex tasks, but coordinating multiple large language models (LLMs) remains riddled with instability, inefficiency, and memory fragmentation. Enter JoyAgents-R1, a novel framework that proposes an elegant, scalable solution for jointly evolving heterogeneous LLM agents using Group Relative Policy Optimization (GRPO). Developed by researchers at JD.com, JoyAgents-R1 combines memory evolution, policy optimization, and clever sampling strategies to form a resilient multi-agent architecture capable of matching the performance of larger SOTA models with far fewer parameters. ...