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Less from More: Reinforcing Sparse Video Reasoning from Dense References
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arXiv Computer Vision
4d ago
Less from More: Reinforcing Sparse Video Reasoning from Dense References
The paper introduces SAVER, a dense-to-sparse post‑training framework that leverages dense video views as training‑time references to improve sparse‑frame inference. By optimizing paired dense and sparse views with grounding and reliability‑gated reference rewards, SAVER learns to preserve task‑relevant temporal evidence while using far fewer frames. Evaluated on three temporal grounding and six video question‑answering benchmarks, SAVER consistently outperforms dense‑frame baselines, matching or surpassing them with substantially reduced frame budgets.
arXiv Machine Learning
Jun 2
Towards Sparse Video Understanding and Reasoning
arXiv:2602. 13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA).
arXiv Computer Vision
Aug 21
Video Evidence to Reasoning Efficient Video Understanding via Explicit Evidence Grounding
arXiv:2601. 07761v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) face a fundamental dilemma in video reasoning: they are caught between the prohibitive computational costs of verbose reasoning and the hallucination risks of efficient, ungrounded approaches.
arXiv Computer Vision
Aug 27
Where to Look Matters: On-Policy Self-Distillation for Long-Video Understanding
The paper introduces Clue-OPSD, a clue‑privileged on‑policy self‑distillation framework that improves long‑video understanding by focusing on short, question‑relevant clue intervals rather than the entire video. Experiments on multiple benchmarks and Qwen3.5 model scales show that this approach consistently outperforms standard backbone models and competes strongly with supervised post‑training baselines, all while requiring fewer input frames and no additional inference modules.
arXiv Computer Vision
Sep 1
Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting
The paper introduces structured video prompting, a training‑free inference‑time technique that augments input videos with lightweight spatial and temporal structure to provide explicit anchors for evidence organization. By applying this method to two video benchmarks and two open video‑language models, the authors demonstrate performance improvements across several tasks, with gains varying by model and task. The study suggests that failures in video‑language models stem not only from reasoning capacity but also from how video evidence is presented during inference.
arXiv Computer Vision
Oct 1
Frame Differential On-Policy Self-Distillation for Video Reasoning
arXiv:2609.39021v1 Announce Type: new Abstract: Reinforcement learning (RL) has substantially improved the reasoning ability of multimodal language models through verifiable rewards and increasingly...