R 4B: Incentivizing General Purpose Auto Thinking Capability in MLLMs via Bi Mode Annealing and Reinforce Learning [📚 Arxiv Paper] [🤗 Hugging Face] [🤖️ ModelScope] [💻 Code] ⭐️ Introduction In this repo, we present R 4B , a multimodal large language model designed for general purpose auto thinking, autonomously switching between step by step thinking and direct response generation based on task complexity. This capability enables R 4B to deliver high quality responses while significantly improving inference efficiency and reducing computational costs. The development of R 4B follows a two stage training paradigm: (1) Bi mode Annealing, which establishes both thinking and non thinking capabilities for VQA; and (2) Bi mode Policy Optimization (BPO), which enables the model to adaptively switch between thinking and non thinking modes based on input demands. 🚀 Key Features 🧠 Think Smart, Act Fast: Adaptive & Controllable Thinking! Our model provides three mode control over the response process. Auto thinking Mode: Unleash auto thinking that works across general topics, from simple Q&A to complex scientific analysis. It saves time and computation by thinking only when it matters. S…
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