aiqbench analysis
This research addresses the credit assignment problem in reinforcement learning for search agents by introducing dense process supervision through fact utility estimation. By converting Bayesian-inferred utilities of discrete evidence facts into step-level rewards, the method significantly improves performance on multi-hop question answering benchmarks compared to outcome-only training.
Dense Process Supervision for Search Agents via Fact Utility Estimation
arXiv:2609.00833v1 Announce Type: cross Abstract: Reinforcement learning (RL) for search agents typically relies on outcome rewards. However, it often fails to achieve effective credit assignment, due to the unclear value of intermediate steps. It is hard to separate their contributions from the final result. In this paper, we propose a dense process supervision method based on fact utility estimation, which models the reasoning process as the accumulation of discrete evidence facts. We first extract structured facts from raw observations and organize them into an explicit fact store. To support credit assignment, we then cluster semantically equivalent facts and infer the posterior utility of each fact cluster using Bayesian estimation over group rollouts. Finally, we convert the estimated fact utilities into dense step-level rewards to guide RL training. Experiments on seven single-hop and multi-hop QA benchmarks show that our method consistently outperforms existing baselines. Ablation studies validate clear relative improvements on multi-hop QA compared to outcome reward-only training.
Abstract:Reinforcement learning (RL) for search agents typically relies on outcome rewards. However, it often fails to achieve effective credit assignment, due to the unclear value of intermediate steps. It is hard to separate their contributions from the final result. In this paper, we propose a dense process supervision method based on fact utility estimation, which models the reasoning process as the accumulation of discrete evidence facts. We first extract structured facts from raw observations and organize them into an explicit fact store. To support credit assignment, we then cluster semantically equivalent facts and infer the posterior utility of each fact cluster using Bayesian estimation over group rollouts. Finally, we convert the estimated fact utilities into dense step-level rewards to guide RL training. Experiments on seven single-hop and multi-hop QA benchmarks show that our method consistently outperforms existing baselines. Ablation studies validate clear relative improvements on multi-hop QA compared to outcome reward-only training.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2609.00833 [cs.CL]
(or arXiv:2609.00833v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.00833 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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