[Submitted on 17 Oct 2026]
Title:Expert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains
View a PDF of the paper titled Expert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains, by Ashish Bansal and 5 other authors
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Abstract:Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent, rewrite queries, and retrieve information under domain-specific constraints. Although reinforcement learning (RL) can train such agents from the consequences of their actions, recent successes largely rely on verifiable tasks with automatically computed binary rewards. In non-verifiable retrieval tasks, assessing query quality requires scarce subject-matter expert (SME) judgment that is inherently graded rather than binary. With Group Relative Policy Optimization (GRPO), sparse rewards yield no useful learning signal when all sampled outputs within a group receive the same outcome, causing optimization to stall. We introduce a method that converts a small, fixed investment of SME judgment into a dense reward and combines it with retrieval-based metrics to form a hybrid, GRPO-compatible training signal. Experiments show improvements of up to 6\% in Recall@3 and 6.7\% in MRR@3 compared with sparse-reward baselines.
Comments: 11 pages, 4 figures, RL4LLM-Agents workshop ICAIF ACM
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
Cite as: arXiv:2610.26932 [cs.LG]
(or arXiv:2610.26932v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.26932
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ashish Bansal [view email] [v1] Sat, 17 Oct 2026 19:01:11 UTC (625 KB)
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