Rethinking RL Scaling for Vision Language Models: A Transparent,
  From-Scratch Framework and Comprehensive Evaluation Scheme
Authors: name: Yan Ma, name: Steffi Chern, name: Xuyang Shen, name: Yiran Zhong, name: Pengfei Liu

Reinforcement learning (RL) has recently shown strong potential in improving
the reasoning capabilities of large language models and is now being actively
extended to vision-language models (VLMs). However, existing RL applications in
VLMs often rely on heavily engineered frameworks that hinder reproducibility
and accessibility, while lacking standardized evaluation protocols, making it
difficult to compare results or interpret training dynamics. This work
introduces a transparent, from-scratch framework for RL in VLMs, offering a
minimal yet functional four-step pipeline validated across multiple models and
datasets. In addition, a standardized evaluation scheme is proposed to assess
training dynamics and reflective behaviors. Extensive experiments on visual
reasoning tasks uncover key empirical findings: response length is sensitive to
random seeds, reflection correlates with output length, and RL consistently
outperforms supervised fine-tuning (SFT) in generalization, even with
high-quality data. These findings, together with the proposed framework, aim to
establish a reproducible baseline and support broader engagement in RL-based
VLM research.