VideoCuRL: Video Curriculum Reinforcement Learning with Orthogonal Difficulty Decomposition

• URL: http://arxiv.org/abs/2601.00887v1
• Date: Wed, 31 Dec 2025 09:25:36 GMT
• Title: VideoCuRL: Video Curriculum Reinforcement Learning with Orthogonal Difficulty Decomposition
• Authors: Hongbo Jin, Kuanwei Lin, Wenhao Zhang, Yichen Jin, Ge Li,
• Abstract summary: Reinforcement Learning (RL) is empowering VideoLLMs with complex scalar reasoning.<n>VideoRL decomposes difficulty into Visual Temporal Load and Cognitive Reasoning Depth.<n>Extensive experiments show that VideoRL surpasses strong baselines on reasoning.
• Score: 18.449209218204782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning (RL) is crucial for empowering VideoLLMs with complex spatiotemporal reasoning. However, current RL paradigms predominantly rely on random data shuffling or naive curriculum strategies based on scalar difficulty metrics. We argue that scalar metrics fail to disentangle two orthogonal challenges in video understanding: Visual Temporal Perception Load and Cognitive Reasoning Depth. To address this, we propose VideoCuRL, a novel framework that decomposes difficulty into these two axes. We employ efficient, training-free proxies, optical flow and keyframe entropy for visual complexity, Calibrated Surprisal for cognitive complexity, to map data onto a 2D curriculum grid. A competence aware Diagonal Wavefront strategy then schedules training from base alignment to complex reasoning. Furthermore, we introduce Dynamic Sparse KL and Structured Revisiting to stabilize training against reward collapse and catastrophic forgetting. Extensive experiments show that VideoCuRL surpasses strong RL baselines on reasoning (+2.5 on VSI-Bench) and perception (+2.9 on VideoMME) tasks. Notably, VideoCuRL eliminates the prohibitive inference overhead of generation-based curricula, offering a scalable solution for robust video post-training.

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