arxiv arXiv cs.AI · 1d ago · research

Fathom-Vaidya improves medical reasoning with rubric-based rewards

from English

The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance both diagnostic and clinical healthcare reasoning. The training framework first targets diagnostic accuracy using MedBullets-derived questions and then addresses multi-turn clinical interactions through 5.3k generated scenarios with multi-dimensional rubrics.

The model achieves 50.1% accuracy on HealthBench-Hard, surpassing proprietary baselines including GPT-5 (thinking).
It demonstrates over a 10% improvement on the MedXpertQA benchmark.
The approach systematically improves complex diagnostic scenarios and contextual dialogue capabilities.

This work demonstrates that targeted synthetic datasets and rubric-based training can effectively address persistent weaknesses in medical LLMs.

Importance 2/3 Beats a top-lab benchmark arXiv cs.AI Research paper Training methods

Benchmarks

Benchmark	Model	Score
HealthBench	Fathom-Vaidya	50.1%

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Fathom-Vaidya improves medical reasoning with rubric-based rewards

The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance diagnostic and clinical healthcare reasoning. The training framework first applies rule-guided RL to MedBullets-derived questions for diagnosis, then utilizes 5.3k synthetic multi-turn scenarios with multi-dimensional rubrics for interactive clinical tasks.

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Fathom-Vaidya improves medical reasoning with rubric-based rewards

The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance both diagnostic and clinical healthcare reasoning.

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