SAIL-RL is a newly developed framework aimed at enhancing the reasoning capabilities of multimodal large language models (MLLMs). Unlike existing methods that focus solely on producing correct answers, SAIL-RL emphasizes guiding models on when and how to think during the reasoning process. This dual-reward reinforcement learning tuning approach helps models avoid unnecessary overthinking on simple tasks, thereby improving efficiency. At the same time, it boosts performance on more complex tasks by encouraging deeper reasoning only when needed. By addressing the limitations of prior techniques, SAIL-RL provides a balanced mechanism that adapts the model’s cognitive effort to the task complexity. This innovation marks a significant step forward in optimizing MLLMs’ reasoning strategies. The framework was detailed in a recent publication on arXiv, highlighting its potential impact on future AI developments.