What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA
arXiv:2605.25988v1 Announce Type: new Abstract: Medical RAG needs evidence-grounded claims, so plugging a claim-level NLI checker into retrieval-augmented RL is intuitive. \textbf{We find that the checker's \emph{output distribution} during training, not its held-out accuracy, decides whether it provides trainable gradient.} We compare four NLI checker back-ends as process rewards inside a GRPO-trained medical RAG agent (Qwen2.5-7B, replicated on Qwen3-4B and Llama-3.1-8B) across four held-out medical QA benchmarks. Three diagnostic findings emerge. \textbf{(i)} Signal collapse is log-prob-spe
This research is timely as the development of reliable and 'trainable' AI agents for high-stakes domains like medicine is critical for their real-world adoption and beneficial impact.
Understanding how to effectively train medical AI agents is crucial for deploying safe and accurate systems, preventing potentially harmful outputs, and accelerating progress in AI-driven healthcare.
The focus for improving medical AI agents shifts from solely held-out accuracy of components to analyzing the output distribution dynamics of those components during training.
- · AI healthcare researchers
- · Medical AI developers
- · Patients benefiting from more reliable AI
- · Developers solely focused on offline component accuracy
Medical RAG systems will be developed with a deeper understanding of checker output distributions.
Improved medical AI accuracy could accelerate drug discovery and diagnostic processes.
More robust and trustworthy medical AI could lead to widespread integration into clinical decision-making, altering healthcare delivery models.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL