PRO-Step: Step-level Process Reward Optimization for Retrieval-Augmented Generation

Abstract:Retrieval-Augmented Generation enhances Large Language Models by grounding responses in external knowledge, but multi-hop reasoning remains vulnerable to error propagation, where early retrieval failures confound subsequent steps. Standard outcome-based optimization only rewards the final answer, leaving intermediate retrieval and reasoning errors undetected. While existing process-based methods introduce step-level signals, they still score each step against the final answer, rewarding spurious successes where flawed retrieval coincidentally produces the correct answer. Step-level supervision in RAG requires evaluating both logical validity and evidential grounding at each step. We introduce PRO-STEP: we train a generative PRM that evaluates both dimensions, employ PRM-guided value tree search to construct preference pairs contrasting valid steps against flawed ones, and optimize the policy via step-level Direct Preference Optimization. Experiments on single and multi-hop QA datasets demonstrate that PRO-STEP achieves the best average EM and F1 across five benchmarks. Code, models, and training data are publicly available at this https URL.

22 pages, 7 figures, 23 tables. Accepted to EMNLP 2026

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.01658 [cs.CL]

(or arXiv:2609.01658v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.01658 Focus to learn more arXiv-issued DOI via DataCite

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