Teach-to-Reason: Competition-Guided Reasoning with a Self-Improving Teacher
Chest X-ray visual question answering (CXR VQA) requires models not only to predict correct answers, but also to produce reliable medical reasoning. However, existing reinforcement-learning-based training typically relies on answer-level rewards, which are often too coarse to improve chain-of-thought (CoT) quality and can become ineffective when group-level advantages collapse to zero. We propose \textbf{Teach-to-Reason (T2R)}, a framework that introduces comparison-based supervision into CoT optimization through a self-improving \emph{Teacher} and a competition-guided \emph{Reasoner}. As the Teacher is iteratively strengthened via self-competition, the Reasoner is optimized against progr...
| Authors | Xiao Han, Hao Liu, Zhimin Bao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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