Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO
This paper proposes a Variance-Aware Reward Framework using Group Relative Policy Optimization (GRPO) for post-training LLMs on heart-focused medical question answering. The method replaces weighted binary criterion aggregation and single Likert scoring with continuous analytical reward functions, providing richer optimization signals. On the heart subset of HealthBench, the best variant improves accuracy from 0.362 to 0.502 and F1 from 0.532 to 0.668 over the Qwen3-14B base model, remaining competitive with GPT-OSS-120B.
[2606.05174] Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO
[Submitted on 17 Apr 2026]
Title:Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO
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Abstract:Large Language Models (LLMs) have shown strong promise in healthcare applications. Yet deploying general-purpose models in real-world settings remains difficult due to data privacy constraints, inference costs, and limited suitability for edge or on-device use. These challenges motivate the development of smaller, more efficient models that require robust post-training strategies to ensure reliable medical reasoning. In this work, we investigate Group Relative Policy Optimization (GRPO) for post-training LLMs on heart-focused medical question answering with rubric-based supervision derived from RaR-Medicine. We propose a Variance-Aware Reward Framework that extends the Explicit Aggregation and Implicit Aggregation strategies of Rubrics as Rewards by replacing weighted binary criterion aggregation and single overall Likert-style scoring with continuous analytical reward functions derived from criterion-level rubric outcomes. This formulation provides richer optimization signals for feedback that is sparse, multi-criteria, and difficult to verify automatically, and enables more stable on-policy reinforcement learning. On a held-out heart-related subset of HealthBench, our best GRPO variant improves accuracy from 0.362 to 0.502 and F1 from 0.532 to 0.668 relative to the Qwen3-14B base model, while remaining competitive with GPT-OSS-120B (0.508 accuracy, 0.674 F1). Our findings show that carefully designed rubric-based rewards provide a practical strategy for improving heart-focused medical question answering in LLMs, with potential to extend to other rubric-based tasks.
Comments: 27 Pages
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.05174 [cs.CL]
(or arXiv:2606.05174v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.05174
arXiv-issued DOI via DataCite
Submission history
From: Yaser Banad [view email] [v1] Fri, 17 Apr 2026 03:08:55 UTC (746 KB)
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