Title: SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward Supervision
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Vision-language Model; Multi-modal Understanding

Abstract: We introduce SAIL-RL, a reinforcement learning~(RL)–based post-training framework that advances the reasoning capability of Multimodal Large Language Models (MLLMs) by teaching them when and how to think. Existing approaches are hindered by outcome-only supervision, which rewards correct answers without sound reasoning, and uniform thinking strategies, which cause overthinking on simple tasks and underthinking on complex ones. SAIL-RL addresses these challenges with a dual reward system: the Thinking Quality Reward supervises how models reason by evaluating factual grounding, logical coherence, and answer consistency, while the Thinking Judge Reward determines when to engage deep reasoning versus direct answering. Experiments on the state-of-the-art SAIL-VL2 demonstrate that SAIL-RL improves reasoning and multimodal understanding benchmarks at both 4B and 8B scales, achieves leading results on OpenCompass, and substantially reduces hallucinations, establishing it as a principled framework for building more reliable and adaptive MLLMs.