ChronoForge-RL: Chronological Forging through Reinforcement Learning for Enhanced Video Understanding
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arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909797644763136 |
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| author | Chen, Kehua |
| author_facet | Chen, Kehua |
| contents | Current state-of-the-art video understanding methods typically struggle with two critical challenges: (1) the computational infeasibility of processing every frame in dense video content and (2) the difficulty in identifying semantically significant frames through naive uniform sampling strategies. In this paper, we propose a novel video understanding framework, called ChronoForge-RL, which combines Temporal Apex Distillation (TAD) and KeyFrame-aware Group Relative Policy Optimization (KF-GRPO) to tackle these issues. Concretely, we introduce a differentiable keyframe selection mechanism that systematically identifies semantic inflection points through a three-stage process to enhance computational efficiency while preserving temporal information. Then, two particular modules are proposed to enable effective temporal reasoning: Firstly, TAD leverages variation scoring, inflection detection, and prioritized distillation to select the most informative frames. Secondly, we introduce KF-GRPO which implements a contrastive learning paradigm with a saliency-enhanced reward mechanism that explicitly incentivizes models to leverage both frame content and temporal relationships. Finally, our proposed ChronoForge-RL achieves 69.1% on VideoMME and 52.7% on LVBench compared to baseline methods, clearly surpassing previous approaches while enabling our 7B parameter model to achieve performance comparable to 72B parameter alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15800 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChronoForge-RL: Chronological Forging through Reinforcement Learning for Enhanced Video Understanding Chen, Kehua Computer Vision and Pattern Recognition Artificial Intelligence Current state-of-the-art video understanding methods typically struggle with two critical challenges: (1) the computational infeasibility of processing every frame in dense video content and (2) the difficulty in identifying semantically significant frames through naive uniform sampling strategies. In this paper, we propose a novel video understanding framework, called ChronoForge-RL, which combines Temporal Apex Distillation (TAD) and KeyFrame-aware Group Relative Policy Optimization (KF-GRPO) to tackle these issues. Concretely, we introduce a differentiable keyframe selection mechanism that systematically identifies semantic inflection points through a three-stage process to enhance computational efficiency while preserving temporal information. Then, two particular modules are proposed to enable effective temporal reasoning: Firstly, TAD leverages variation scoring, inflection detection, and prioritized distillation to select the most informative frames. Secondly, we introduce KF-GRPO which implements a contrastive learning paradigm with a saliency-enhanced reward mechanism that explicitly incentivizes models to leverage both frame content and temporal relationships. Finally, our proposed ChronoForge-RL achieves 69.1% on VideoMME and 52.7% on LVBench compared to baseline methods, clearly surpassing previous approaches while enabling our 7B parameter model to achieve performance comparable to 72B parameter alternatives. |
| title | ChronoForge-RL: Chronological Forging through Reinforcement Learning for Enhanced Video Understanding |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.15800 |