LAPO: Self-Generated Process Rewards for Multi-Turn Search Reasoning

Jul 16, 2026

A new method called LAPO introduces self-generated process supervision for multi-turn search reasoning by using backward leave-one-turn attribution. LAPO estimates the contribution of each search turn by measuring the change in the policy's likelihood of the correct answer when a turn is removed, and applies sign-consistency gating to refine these process rewards. Tested across seven knowledge-intensive QA datasets, LAPO achieves an average exact-match score of 0.326, outperforming the IGPO baseline by 0.053, without requiring additional reward models or external supervision.

Why it matters: LAPO demonstrates a practical approach to improving multi-turn search reasoning by enabling fine-grained process supervision using only the policy itself, potentially reducing reliance on external resources.

Full story at: arXiv AI/ML ↗