SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
We introduce SAIL-RL, a reinforcement learning (RL) post-training framework that enhances the reasoning capabilities of multimodal large language models (MLLMs) by teaching them when and how to think. Existing approaches are limited by outcome-only supervision, which rewards correct answers without ensuring sound reasoning, and by uniform thinking strategies, which often lead to overthinking on simple tasks and underthinking on complex ones. SAIL-RL addresses these challenges with a dual reward system: the Thinking Reward, which evaluates reasoning quality through factual grounding, logical coherence, and answer consistency, and the Judging Reward, which adaptively determines whether deep reasoning or direct answering is appropriate. Experiments on the state-of-the-art SAIL-VL2 show that SAIL-RL improves reasoning and multimodal understanding benchmarks at both 4B and 8B scales, achieving competitive performance against commercial closed-source models such as GPT-4o, and substantially reduces hallucinations, establishing it as a principled framework for building more reliable and adaptive MLLMs. The code will be available at https://github.com/BytedanceDouyinContent/SAIL-RL.
690c18d960494e4fa7675677
Fangxun Shu
690c18d960494e4fa7675678
Yongjie Ye
690c18d960494e4fa7675679
Yue Liao
690c18d960494e4fa767567a
Zijian Kang
690c18d960494e4fa767567b
Weijie Yin
690c18d960494e4fa767567c
Jiacong Wang
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2026-03-12T09:22:32.211Z
64d201b1c2bd235422fb1d14
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wang
stormthunder
user
stormthunder
690c18d960494e4fa767567d
Xiao Liang
690c18d960494e4fa767567e
Shuicheng Yan
690c18d960494e4fa767567f
Chao Feng
SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
We introduce SAIL-RL, a reinforcement learning (RL) post-training framework that enhances the reasoning capabilities of multimodal large language models (MLLMs) by teaching them when and how to think. Existing approaches are limited by outcome-only supervision, which rewards correct answers without ensuring sound reasoning, and by uniform thinking strategies, which often lead to overthinking on simple tasks and underthinking on complex ones. SAIL-RL addresses these challenges with a dual reward system: the Thinking Reward, which evaluates reasoning quality through factual grounding, logical coherence, and answer consistency, and the Judging Reward, which adaptively determines whether deep reasoning or direct answering is appropriate. Experiments on the state-of-the-art SAIL-VL2 show that SAIL-RL improves reasoning and multimodal understanding benchmarks at both 4B and 8B scales, achieving competitive performance against commercial closed-source models such as GPT-4o, and substantially reduces hallucinations, establishing it as a principled framework for building more reliable and adaptive MLLMs. The code will be available at https://github.com/BytedanceDouyinContent/SAIL-RL.
SAIL-RL enhances multimodal large language models' reasoning capabilities using a dual reward system that improves factual grounding, logical coherence, and adaptability.
reinforcement learning multimodal large language models Thinking Reward Judging Reward factual grounding logical coherence answer consistency deep reasoning direct answering reasoning benchmarks multimodal understanding benchmarks hallucinations