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arXiv · 2610.15098

TIDE: Trajectory-Informed Skill Distillation for Efficient Text-to-Video Prompt Optimization

Chengxing Zhou·Yuliang Huang·Jingwei Song
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Abstract

Text-to-video (T2V) models are highly sensitive to prompt specificity, while iterative prompt optimization typically improves generation quality only through repeated video generation and vision-language model evaluation at inference time. We propose TIDE, a framework that distills this costly search process into an interpretable and reusable prompt-rewriting Skill. TIDE formulates prompt rewriting as a set of source-conditioned rule modules, each targeting a distinct semantic dimension. Once distilled, the frozen Skill activates the relevant rules and generates both positive and negative prompts in a single LLM call, followed by only one T2V generation, without test-time visual search or backbone fine-tuning. Evolved using only 100 prompts over 14 hours, TIDE achieves prompt-optimization performance comparable to computationally expensive search-based methods. Moreover, when transferred to previously unseen video-generation models, TIDE outperforms existing approaches specifically designed or trained for those models. Code is available at https://github.com/xiningin/TIDE_T2V.

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Chengxing Zhou, Yuliang Huang, Jingwei Song. 2026-10-10. TIDE: Trajectory-Informed Skill Distillation for Efficient Text-to-Video Prompt Optimization. https://arxiv.org/abs/2610.15098

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