What if diverse AI agents could mutually learn and improve without sacrificing their autonomy? Researchers from Beihang University, Bytedance China, Tsinghua University, and Peking University have just unveiled Heterogeneous Agent Collaborative Reinforcement Learning (HACRL)! This innovative framework allows different types of AI agents to share verified learning experiences during training, creating a bidirectional flow of knowledge to enhance performance for everyone. Unlike other multi-agent systems, it requires no coordinated deployment and fosters true peer-to-peer growth, not one-way teaching. Their HACPO algorithm consistently boosts all participating agents, outperforming GSPO by 3.3% on diverse reasoning benchmarks while dramatically cutting training data costs in half. Heterogeneous Agent Collaborative Reinforcement Learning Paper: arxiv.org/abs/2603.02604 Github Page: zzx-peter.github.io/hacrl/ Huggingface: huggingface.co/papers/2603.0… Our report: mp.weixin.qq.com/s/ggzim_4Pc… 📬 #PapersAccepted by Jiqizhixin

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