<ul><li>Recent advancements in Chain of Thought (COT) generation have improved the reasoning capabilities of Large Language Models (LLMs).</li><li>SEED-Bench-R1 is a benchmark designed to evaluate post-training methods for Multimodal Large Language Models (MLLMs) in video understanding.</li><li>Reinforcement Learning (RL) shows data efficiency and superior performance on both in-distribution and out-of-distribution tasks compared to supervised fine-tuning (SFT).</li><li>However, RL often produces less logically coherent reasoning chains and has limitations such as inconsistent reasoning and overlooked visual cues.</li></ul>

Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1

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