R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs
  via Bi-Mode Annealing and Reinforce Learning
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.
68b4f36d851c6e7b001ec9c5
Jie Jiang
68b4f36d851c6e7b001ec9c6
Qi Yang
claimed_verified
2025-09-03T08:29:38.945Z
643e45c4c639f8bc9727810a
https://cdn-avatars.huggingface.co/v1/production/uploads/643e45c4c639f8bc9727810a/BJR1cvSCxcqxr08iS7GsI.jpeg
YannQi
user
YannQi
68b4f36d851c6e7b001ec9c7
Bolin Ni
claimed_verified
2025-09-04T08:44:25.650Z
642149592cc2b3c39e7ef6c9
/avatars/fed5d447375cd8c3b2bc9e096fe681de.svg
Bolin Ni
user
nbl97
68b4f36d851c6e7b001ec9c8
Shiming Xiang
68b4f36d851c6e7b001ec9c9
Han Hu
68b4f36d851c6e7b001ec9ca
Houwen Peng
R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs
  via Bi-Mode Annealing and Reinforce Learning
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.
R-4B, an auto-thinking multimodal large language model, uses bi-mode annealing and Bi-mode Policy Optimization to adaptively decide on problem-solving strategies, achieving state-of-the-art performance with lower computational cost.
multimodal large language models step-by-step thinking auto-thinking bi-mode annealing Bi-mode Policy Optimization GRPO framework policy model reasoning-intensive benchmarks