Evo-PI: Aligning Medical Reasoning via Evolving Principle-Guided Supervision
Despite recent progress, the reasoning capabilities of large multimodal language models (MLLMs) remain fundamentally constrained by static supervision, where fixed prompts, rules, or reward models provide non-adaptive guidance throughout training. Such static signals are often sufficient to enforce output formats, but fail to shape the underlying reasoning process, leading to brittle generalization and performance saturation in complex decision-making tasks. We propose Evo-PI, a principle-centric learning framework that treats reasoning principles as explicit, language-based supervision signals that can be generated, evaluated, and iteratively evolved. Instead of relying on fixed rewards,...
| Authors | Xianda Zheng, Huan Gao, Meng-Fen Chiang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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