<ul><li>Researchers propose a new approach for enhancing reasoning capabilities in Multimodal Large Language Models (MLLMs).</li><li>Effective cold start initialization is identified as crucial for improving MLLM reasoning, even before applying multimodal reinforcement learning.</li><li>Standard GRPO used in multimodal reinforcement learning faces issues like gradient stagnation, impacting training stability and performance.</li><li>A staged training approach called ReVisual-R1 is introduced, achieving a new state-of-the-art performance on various challenging benchmarks.</li></ul>

Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning

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