arXiv:2510.17045v2 Announce Type: replace-cross Abstract: Video reasoning using Large Multimodal Models (LMMs) relies on costly reinforcement learning (RL) and verbose chain-of-thought, resulting in substantial computational overhead during both training and inference. Moreover, the mechanisms that control the thinking process in these reasoning models are very limited. In this paper, we use the entropy of the model's output distribution as a signal to study and guide reasoning behavior. We discover that high-quality models exhibit a characteristic pattern of micro-exploration and micro-exploi
Efforts to improve efficiency and reasoning capabilities in large multimodal models (LMMs) for video are accelerating, driven by the desire to overcome current computational and methodological limitations.
This research suggests a more efficient and effective path for developing video reasoning AI, potentially reducing the computational costs and improving the interpretability and control of LMMs.
Traditional reliance on costly reinforcement learning and verbose chain-of-thought for video reasoning might be replaced by methods leveraging entropy for behavioral guidance, making model development more resource-efficient.
- · AI developers
- · Cloud providers (reduced inference costs)
- · Academia (new research avenues)
- · Developers solely reliant on current RL/CoT methods
More sophisticated and computationally cheaper video reasoning AI models become feasible.
Broader adoption of LMMs in applications requiring complex video analysis due to lower operational costs.
Enhanced AI capabilities in areas like autonomous systems, surveillance, and content generation, pushing the frontier of AI agentic behavior.
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Read at arXiv cs.LG