Title: VOLD: Reasoning Transfer from LLMs to Vision-Language Models via On-Policy Distillation
Primary area: foundation or frontier models, including LLMs
Keywords: Reasoning, Multimodal reasoning, Math vision

Abstract: Training vision-language models (VLMs) for complex reasoning remains a challenging task, i.a. due to the scarcity of high-quality image-text reasoning data. Conversely, text-based reasoning resources are abundant and scalable, but it is still an open question how to leveraging them for VLM reasoning. 
To address this problem, we propose VOLD, a framework to transfer reasoning capabilities from text-only teacher models to VLM student models. 
To this end, VOLD combines reinforcement learning via Group Relative Policy Optimization (GRPO) with on-policy distillation, which allows the student reasoning traces to be guided by the teacher model, resulting in a significant gain over using GRPO alone.
We further show that a cold-start alignment is essential for an effective transfer during the online training phase in this scenario and that without sufficient distributional alignment between teacher and student, on-policy distillation fails to provide meaningful guidance.
We evaluate VOLD across diverse benchmarks including MMMU-Pro, MathVision, MathVista, and LogicVista, showing that VOLD outperforms the baseline model significantly and improves over 
the state of the art by a margin.
Our ablation shows the importance of a cold-start alignment via SFT for on-policy distillation with a text-only teacher.

TL;DR: We propose VOLD, a framework that transfers reasoning from text-only teachers to vision-language students via unified RL and on-policy distillation.