Deep Thought Alignment: Trajectory-Level Latent Distillation for Video Reasoning
Quick summary
arXiv:2608.16316v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) for video reasoning have long been hindered by the high computational cost of processing vast amounts of visual information. This dilemma motivates the transfer of the reasoning capabilities of large models to smaller, more efficient ones. On-Policy Distillation (OPD) offers a promising solution by matching output-token distributions along student-generated trajectories. However, video reasoning often depends on evidence accumulated across multiple frames. In this context, output-level supervision only captures in
Key takeaways
- arXiv:2608.16316v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) for video reasoning have long been hindered by the high computational cost of processing vast amounts of visual information.
- This dilemma motivates the transfer of the reasoning capabilities of large models to smaller, more efficient ones.
- On-Policy Distillation (OPD) offers a promising solution by matching output-token distributions along student-generated trajectories.
Why it matters
“Deep Thought Alignment: Trajectory-Level Latent Distillation for Video Reasoning” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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