Core Viewpoint - The rapid development of multi-modal large language models (MLLMs) is significantly enhancing video reasoning capabilities, driven by reinforcement learning (RL) as a key engine for this technological revolution [1] Group 1: TW-GRPO Framework Introduction - The TW-GRPO framework is proposed to address challenges in reasoning quality and reward granularity in video reasoning tasks, inspired by the traditional GRPO framework [2] - TW-GRPO integrates focused thinking and multi-level soft reward mechanisms for multi-choice QA tasks [3] Group 2: Key Improvements in TW-GRPO - The framework enhances information weighting and reward mechanism design, applying a soft reward mechanism from video localization to video reasoning tasks [4] - A dynamic weighting mechanism prioritizes high information density tokens, improving reasoning accuracy and efficiency by focusing on key content [4] - The multi-level reward mechanism redefines rewards, allowing for partial correctness in answers, thus improving training stability and efficiency [5] Group 3: Data Augmentation and Training Efficiency - TW-GRPO introduces a question-answer inversion (QAI) data augmentation technique to convert single-choice tasks into multi-choice formats, effectively expanding the training data pool [6] - This approach disrupts traditional equal treatment of tokens, enhancing training efficiency and reasoning performance through differentiated information processing [6] Group 4: Experimental Validation - Extensive experiments demonstrate TW-GRPO's effectiveness in video reasoning and general understanding tasks, outperforming Video-R1 by 18.8%, 1.8%, and 1.6% in various benchmarks [12][15] - The framework shows faster convergence and more stable learning processes compared to traditional GRPO, with shorter output sequences indicating more efficient reasoning [11][17] Group 5: Qualitative Analysis of Reasoning Paths - A qualitative comparison of reasoning paths between T-GRPO and TW-GRPO illustrates significant improvements in accuracy and efficiency in dynamic visual cue reasoning tasks [22]