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# Efficient Video Reasoning with Dual-Answer Training **Authors:** Shuming Liu, **Source:** [HuggingFace](https://huggingface.co/papers/2601.05175) | [arXiv](https://arxiv.org/abs/2601.05175) **Published:** 2026-01-09 **Organization:** Hugging Face ## Summary - Introduces a “reason‑when‑necessary” policy that triggers deep reasoning only for ambiguous video frames, reducing unnecessary computation. - Proposes a “Thinking Once, Answering Twice” paradigm where the model generates an intermediate reasoning trace before producing two complementary answers, improving answer consistency. - Utilizes verifiable reward signals derived from answer agreement and reasoning coherence to train the model without requiring external supervision. - Employs a confidence‑based activation mechanism at inference time, enabling the system to decide autonomously whether to invoke the reasoning module. ## Abstract VideoAuto-R1 framework employs a reason-when-necessary strategy for video understanding, using a Thinking Once, Answering Twice training paradigm with verifiable rewards and confidence-based reasoning activation during inference. --- *Topics: computer-vision, multimodal, reinforcement-learning* *Difficulty: advanced* *Upvotes: 10*