arXiv:2605.06094v4 Announce Type: replace-cross Abstract: Training VideoLLMs for complex reasoning remains challenging due to sparse sequence level rewards and the lack of fine grained credit assignment over long, temporally grounded reasoning trajectories. While reinforcement learning with verifiable rewards (RLVR) provides reliable supervision, it fails to capture token level contributions, leading to inefficient learning. Conversely, existing self distillation methods offer dense supervision but lack structure and diagnostic specificity, and often interact unstably with reinforcement learni
The continuous development in AI research highlights ongoing efforts to overcome challenges in complex AI reasoning, particularly for VideoLLMs, by enhancing training methods like structured self-distillation.
Improving video reasoning capabilities is crucial for the advancement of autonomous AI systems, impacting a wide range of applications from real-time analytics to robotic control.
The proposed VISD method offers a structured approach to self-distillation, potentially leading to more efficient and stable training of VideoLLMs by addressing the limitations of sparse rewards and unstable reinforcement learning.
- · AI research institutions
- · VideoLLM developers
- · AI agent developers
- · Computer vision sector
- · Inefficient AI training methodologies
- · Legacy video analysis systems
More robust and capable VideoLLMs are developed.
Advanced AI agents gain improved understanding and interaction with dynamic visual environments.
Accelerated deployment of AI in complex, real-world monitoring and control scenarios.
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Read at arXiv cs.AI