Edit, Don't Imitate: Minimal-Edit On-Policy Distillation for Multimodal Reasoning

The paper introduces a minimal‑edit on‑policy distillation method for vision‑language models to correct misread diagrams in visual mathematical reasoning. By using reference figure descriptions and verified solutions only during training, the teacher evaluates student rollouts and penalizes only the tokens that deviate from correct reasoning, while correct rollouts receive reinforcement learning rewards. The approach eliminates the need for figure descriptions at test time, improves low‑level geometric perception, and transfers well to external math and perception benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• reinforcement-learning
• efficiency
• multimodal
• benchmarks