Heterogeneous Agent Collaborative Reinforcement Learning
We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem that addresses the inefficiencies of isolated multi-agent on-policy optimization. HACRL enables collaborative optimization with independent execution: heterogeneous agents share verified rollouts during training to mutually improve, while operating independently at inference time. Unlike LLM-based multi-agent reinforcement learning (MARL), HACRL does not require coordinated deployment, and unlike on-/off-policy distillation, it enables bidirectional mutual learning among heterogeneous agents rather than one-directional homogeneous teacher-to-stu...
| Authors | Zhixia Zhang, Zixuan Huang, Gongxun Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 21 May 2026 |
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