{"slug": "expert-guided-rubrics-as-dense-reward-signals-for-reinforcement-learning-in-non", "title": "Expert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains", "summary": "A method that converts a small, fixed investment of subject-matter expert (SME) judgment into a dense reward signal, combined with retrieval-based metrics, improved retrieval agents trained with Group Relative Policy Optimization (GRPO) by up to 6% in Recall@3 and 6.7% in MRR@3 versus sparse-reward baselines, according to the arXiv paper 2610.26932v1. The authors target non-verifiable domains such as financial services, where query quality is graded rather than binary and sparse rewards stall GRPO optimization when all sampled outputs in a group receive the same outcome. The work matters because it offers a GRPO-compatible hybrid training signal for tasks that lack automatically computed binary rewards.", "body_md": "arXiv:2610.26932v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/expert-guided-rubrics-as-dense-reward-signals-for-reinforcement-learning-in-non", "canonical_source": "https://arxiv.org/abs/2610.26932", "published_at": "2026-10-21 04:00:00+00:00", "updated_at": "2026-10-21 04:21:09.771738+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-agents", "ai-research"], "entities": ["Group Relative Policy Optimization", "arXiv", "Recall@3", "MRR@3"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/expert-guided-rubrics-as-dense-reward-signals-for-reinforcement-learning-in-non", "markdown": "https://wpnews.pro/news/expert-guided-rubrics-as-dense-reward-signals-for-reinforcement-learning-in-non.md", "text": "https://wpnews.pro/news/expert-guided-rubrics-as-dense-reward-signals-for-reinforcement-learning-in-non.txt", "jsonld": "https://wpnews.pro/news/expert-guided-rubrics-as-dense-reward-signals-for-reinforcement-learning-in-non.jsonld"}}