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AIPO introduces a novel reinforcement learning framework that enhances large language model reasoning by facilitating active interactions with specialized collaborative agents during the exploration phase. This method addresses sample inefficiency in current RLVR approaches by providing fine-grained, state-dependent guidance to overcome intermediate reasoning bottlenecks before enabling independent policy performance. The technique demonstrates robust improvements across diverse benchmarks including mathematics, coding, and scientific reasoning without requiring continuous external agent support after training.
AIPO: Learning to Reason from Active Interaction
arXiv:2605.08401v3 Announce Type: replace Abstract: 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 interaction during exploration. Specifically, when encountering reasoning bottlenecks, AIPO enables the policy model to proactively consult three functional collaborative agents, namely the verify agent, knowledge agent, and reasoning agent, thereby obtaining fine-grained and state-dependent guidance during rollout. The resulting mixed-policy trajectories expose the policy to reasoning directions that may be difficult to discover through isolated on-policy exploration. To learn effectively from collaborator-provided tokens, we further introduce a corrected importance sampling coefficient together with a lower-bound clipping strategy to mitigate off-policy discrepancy and vanishing gradients. After training, the policy model reasons independently without relying on collaborative agents. Extensive experiments across mathematical, scientific, coding, and puzzle reasoning benchmarks show that AIPO consistently improves reasoning performance and generalizes across different policy models, collaborator backbones, and RLVR algorithms.
Abstract: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 interaction during exploration. Specifically, when encountering reasoning bottlenecks, AIPO enables the policy model to proactively consult three functional collaborative agents, namely the verify agent, knowledge agent, and reasoning agent, thereby obtaining fine-grained and state-dependent guidance during rollout. The resulting mixed-policy trajectories expose the policy to reasoning directions that may be difficult to discover through isolated on-policy exploration. To learn effectively from collaborator-provided tokens, we further introduce a corrected importance sampling coefficient together with a lower-bound clipping strategy to mitigate off-policy discrepancy and vanishing gradients. After training, the policy model reasons independently without relying on collaborative agents. Extensive experiments across mathematical, scientific, coding, and puzzle reasoning benchmarks show that AIPO consistently improves reasoning performance and generalizes across different policy models, collaborator backbones, and RLVR algorithms.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2605.08401 [cs.CL]
(or arXiv:2605.08401v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.08401 Focus to learn more arXiv-issued DOI via DataCite
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