Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents
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Computer Science > Computation and Language
arXiv:2610.10179 (cs)
[Submitted on 7 Oct 2026]
Title:Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents
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Abstract:Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions. Reinforcement Learning with Verifiable Rewards (RLVR) offers a promising approach for post-training such agents, but its reliance on sparse, outcome-based supervision can make credit assignment difficult and limit learning efficiency. In this paper, we systematically investigate how intermediate supervision can improve reinforcement learning for search agents. We study a range of reward-shaping and credit-assignment strategies that provide learning signals from intermediate retrieval steps. Building on these insights, we develop a training framework that combines intermediate signals with final outcome rewards to improve learning from multi-step search trajectories. Experiments across multiple benchmarks under matched training conditions demonstrate improvements in aggregate search-agent performance and show that both the choice of intermediate signal and where its credit is assigned affect training behaviour. These findings show that reward design and credit assignment are important design dimensions for training effective search agents.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.10179 [cs.CL] |
| (or arXiv:2610.10179v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10179
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents, by Wenyu Huang and 4 other authors
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