Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuning

This paper investigates the thinking process in rule-based reinforcement learning fine-tuning (RFT) for multi-modal large language models (MLLMs). We first propose CLS-RL for classification, using verifiable rewards to encourage MLLM thinking. Experiments show CLS-RL significantly outperforms SFT and yields a 'free-lunch' generalization effect (improving performance on unseen datasets after training on one dataset). We then question if this explicit thinking is always necessary for RFT. Challenging convention that explicit thinking is crucial for RFT, we introduce No-Thinking-RL, minimizing thinking via a simple equality accuracy reward. Experiments show No-Thinking-RL surpasses CLS-RL in in-domain and generalization abilities, with significantly less fine-tuning time. This suggests reducing thinking can improve MLLM fine-tuning efficiency and effectiveness for certain visual tasks. We hypothesize explicit thinking negatively impacts reward convergence during RFT. To test this, we propose the Think-After-Answerwer method to let models first output the answer and then generate thinking process to alliviate the negative impact of thinking. We further test No-Thinking-RL on diverse tasks (including math, spatial, puzzles) with 2B and 7B models. For 2B models, No-Thinking-RL outperforms thinking-based RFT for all tasks, even on math, with Think-After-Answerwer performing intermediately. For 7B models, performance is comparable on simple visual tasks, but RFT with thinking excels on complex reasoning (math). This implies when dealing with complex math problems, smaller models struggle with generating effective reasoning, hurting performance on complex tasks. Conversely, for simple visual tasks, thinking is not indispensable, and its removal can boost performance and reduce training time. We hope our findings offer insights for better understanding the effect of the thinking process in RFT.