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The paper introduces CoFire, a reinforcement learning framework for video understanding that addresses the problem of confidence erosion—where initial predictions lose reliability during iterative refinement. CoFire enforces a "confident first, reflective second" principle, ensuring the initial answer remains trustworthy and that subsequent reflection only improves upon it. The method achieves state‑of‑the‑art accuracy on Video‑MME, MLVU, and LongVideoBench, outperforming prior 7B models by 1.7 points and improving both initial and final accuracy over outcome‑reward RL by 4.1 and 3.7 points respectively.
arXiv:2606. 27922v1 Announce Type: cross Abstract: Current multimodal reflection mechanisms for long video understanding predominantly rely on closed-loop self-reflection within internal parameters.
arXiv:2610.01973v1 Announce Type: new Abstract: Reinforcement learning (RL) for video generation usually assigns one scalar reward to an entire sampled video. Yet a video is not uniformly flawed: som...
The paper introduces RARM, a Reference‑Anchored Reward Model that uses a single successful demonstration to generate dense, progress‑aware rewards for reinforcement learning in robot manipulation. RARM is trained on general‑purpose videos with a contrastive temporal objective, requiring no task‑specific data or reward engineering. During deployment it matches rollout clips to reference clips and rewards only confident forward progress, reducing false positives. Experiments on nine simulated tasks and four real‑world tasks show that RARM achieves the best overall success rates, especially on long‑horizon tasks like cloth folding.
arXiv:2608. 04964v1 Announce Type: new Abstract: Interactive video world models are essential for long-horizon planning and exploration, yet they suffer from compounding errors.
arXiv:2606. 29892v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become indispensable for pushing Vision-Language-Action Models (VLAs) beyond static imitation learning.