AIPO: Learning to Reason from Active Interaction
Recent advances in LLMs have demonstrated strong reasoning capabilities, largely stimulated by RLVR. However, the exploration of existing RLVR algorithms remains largely constrained by the knowledge and reasoning strategies already accessible to the policy model. Although recent methods introduce external expert demonstrations to broaden exploration, they typically rely on complete trajectory-level guidance, which can be sample-inefficient, information-sparse, and insufficiently adaptive to intermediate reasoning bottlenecks. Inspired by collaborative multi-agent systems, we propose AIPO, an enhanced reinforcement learning framework that improves LLM reasoning through active multi-agent i...
| Authors | Junnan Liu, Linhao Luo, Thuy-Trang Vu et al. |
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| Primary category | cs.CL |
| Published | 8 May 2026 |
| Last updated at source | 27 September 2026 |
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