Think or Not? Selective Reasoning via Reinforcement Learning for Vision-Language Models
Jiaqi Wang, Kevin Qinghong Lin, James Cheng, Mike Zheng Shou
Reinforcement Learning (RL) has proven to be an effective post-training strategy for enhancing reasoning in vision–language models (VLMs). Group Relative Policy Optimization (GRPO) is a recent prominent method that encourages models to generate complete reasoning traces before answering, leading to increased token usage and computational cost. Inspired by the human-like thinking process—where people skip reasoning for easy questions but think carefully when needed—we explore how to enable VLMs to first decide *when reasoning is necessary*.To realize this, we propose \ours, a two-stage training strategy:**(i)** a supervised fine-tuning (SFT) stage with a simple yet effective “**thought dropout**” operation, where reasoning traces are randomly replaced with empty thoughts. This introduces a think-or-not format that serves as a cold start for selective reasoning;  **(ii)** a GRPO stage that enables the model to freely explore when to think or not, while maximizing task-aware outcome rewards.Experimental results show that \ours can *reduce the completion length by up to **90%** compared to vanilla GRPO, without sacrificing performance or even improving it*. Further evaluations across diverse vision-language tasks—covering a range of reasoning difficulties under both 3B and 7B models—consistently reveal that the *model progressively learns to bypass unnecessary reasoning steps as training advances*. These findings shed light on the path toward human-like reasoning patterns in reinforcement learning approaches.