OpenCoF: Learning to Reason Through Video Generation
Summary
OpenCoF introduces a reasoning video dataset and a fine-tuned video generation model that improves temporal reasoning through diverse supervision and explicit reasoning tokens, showing significant gains on four video reasoning benchmarks.
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Paper page - OpenCoF: Learning to Reason Through Video Generation
Source: https://huggingface.co/papers/2607.08763
Abstract
OpenCoF framework introduces a reasoning video dataset and model that improve temporal reasoning through diverse supervision and explicit reasoning tokens for visual and textual cues.
Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recentvideo generation modelsoffer a reasoning path distinct from previous Chain-of-Thought (CoT): reasoning can unfold through temporally connected frames, known asChain-of-Frame(CoF) reasoning. However, existing video generators are primarily trained on general video corpora, still lacking diverse supervision and dedicated designs for CoF reasoning. To address this gap, we introduce OpenCoF, a framework comprising theOpenCoF-17K dataset, a reasoning video dataset spanning 11 task families, and Wan-CoF, a fine-tuned video model for studying whether diversetemporal supervisionimproves CoF behavior. Across fourvideo reasoning benchmarks, Wan-CoF achieves considerable gains over the Wan2.2-I2V-A14B baseline. Building on this, we empirically explore more advanced designs for CoF capabilities, i.e., equipping the model with visual andtextual reasoning tokens. This mechanism respectively captures low-level visual cues and high-level semantic priors for spatial and temporal reasoning. Through performance comparisons andattention analysis, we examine how these tokens contribute across model depth,denoising steps, space, and time. Our results suggest that stronger video reasoning requires both broadtemporal supervisionand explicit mechanisms for organizing intermediate reasoning state. We open-source the dataset, model, and code to facilitate future research on reasoning-oriented video generation.
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