R-4B: Incentivizing General-purpose Auto-thinking Capability In Mllms Via Bi-mode Annealing And Reinforce Learning | Awesome LLM Papers Add your paper to Awesome LLM Papers

R-4B: Incentivizing General-purpose Auto-thinking Capability In Mllms Via Bi-mode Annealing And Reinforce Learning

Jie Jiang, Qi Yang, Bolin Ni, Shiming Xiang, Han Hu, Houwen Peng . No Venue 2025

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Compositional Generalization Datasets Efficiency Image Text Integration Interdisciplinary Approaches Multimodal Semantic Representation Tools Training Techniques Visual Contextualization

Multimodal Large Language Models (MLLMs) equipped with step-by-step thinking capabilities have demonstrated remarkable performance on complex reasoning problems. However, this thinking process is redundant for simple problems solvable without complex reasoning. To address this inefficiency, we propose R-4B, an auto-thinking MLLM, which can adaptively decide when to think based on problem complexity. The central idea of R-4B is to empower the model with both thinking and non-thinking capabilities using bi-mode annealing, and apply Bi-mode Policy Optimization~(BPO) to improve the model’s accuracy in determining whether to activate the thinking process. Specifically, we first train the model on a carefully curated dataset spanning various topics, which contains samples from both thinking and non-thinking modes. Then it undergoes a second phase of training under an improved GRPO framework, where the policy model is forced to generate responses from both modes for each input query. Experimental results show that R-4B achieves state-of-the-art performance across 25 challenging benchmarks. It outperforms Qwen2.5-VL-7B in most tasks and achieves performance comparable to larger models such as Kimi-VL-A3B-Thinking-2506 (16B) on reasoning-intensive benchmarks with lower computational cost.

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