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LVLMs are better at spotting their own mistakes than generating correct answers in the first place, and this self-awareness can be exploited to reduce hallucinations.
By co-evolving experts through bidirectional policy distillation, CoPD achieves all-in-one integration of text, image, and video reasoning, outperforming domain-specific experts and suggesting a new training paradigm.
Forget external teachers – the best way to boost your RL model's performance is to learn from its future self.
EasyVideoR1 achieves a 1.47 times throughput improvement in video understanding tasks by eliminating redundant video decoding and leveraging a comprehensive task-aware reward system.
Self-distillation in LLMs can leak information and destabilize training, but combining it with verifiable rewards yields a sweet spot for improved convergence and stability.
Fake news in short videos often betrays itself through subtle inconsistencies between text, visuals, and audio, a weakness MAGIC3 exploits to achieve VLM-level accuracy at a fraction of the cost.
