Skill Distillation

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10 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

TIDE: Trajectory-Informed Skill Distillation for Efficient Text-to-Video Prompt Optimization
Oct 10, 2026Chengxing Zhou, Yuliang Huang, Jingwei SongPrompt Optimization for T2V Generation Skill Distillation
Sun Yat-sen University, China · The University of Hong Kong, China
Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution
Oct 8, 2026Yuhan Liu, Xiyao Ma, Zhongkai Sun +4Skill Distillation LLM Agent Skill Learning
New York University · Amazon
From Solo to Ensemble: A Hierarchical Framework for Composable Multi-Agent Human-Object Interaction
Oct 8, 2026Zekai Deng, Kangyi Chen, Ye Shi +1Skill Distillation HOI Generation
ShanghaiTech University · InstAdapt
Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation
Sep 28, 2026Xin Li, Hao Jiang, Xin Gao +6Language Model Distillation Multi-Teacher Knowledge Distillation
Nanyang Technological University · Yale University · University of Manchester
P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems
Aug 14, 2026Myunghoon Ryu, Geunpyo Park, Sungjoon Lee +2Skill Distillation Privacy-Preserving Language Models
Department of Electrical and Computer Engineering, Korea University, Seoul, Republic of Korea
Search2Skill: Skill Distillation Beyond Knowledge Boundaries Via Rubric-Based Reinforcement Learning
Aug 5, 2026Muyang Ye, Tian Lan, Feihu Jiang +10Skill Distillation LLM Agent Skill Learning
Zhejiang University · Alibaba Group · Institute of Artificial Intelligence, Xiamen University +1
FinReportBench: Measuring and Improving Institution-Grade Financial Report Generation
Aug 5, 2026Yinghao Tang, Tan Zhenwei, Yiyao Wang +4Financial Services LLM Evaluation
State Key Laboratory of CAD&CG, Zhejiang University · Ant Group
SKILL-KD: Contrastive Skill Distillation for LLM Agents
Jul 30, 2026Qiming Shi, Yibo Dou, Jiawen Zhu +5Skill Distillation LLM Agent Skill Learning
State Key Lab of CAD&CG, Zhejiang University · School of Software and Microelectronics, Peking University · School of Software Technology, Zhejiang University +1
Scalable Behaviour Cloning on Browser Using via Skill Distillation
Jun 30, 2026Kaisen Yang, Zheng Jiang, Yuzhao Peng +11Skill Distillation Behavior Cloning
Einsia
Evidence Over Plans: Online Trajectory Verification for Skill Distillation
May 9, 2026Yang Zhou, Zihan Dong, Zhenting Wang +7Skill Distillation Agent Evaluation
Rutgers University

Oct 10, 2026J/K move · Enter open · S save

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

Chengxing Zhou, Yuliang Huang, Jingwei Song

Sun Yat-sen University, China · The University of Hong Kong, China

Prompt Optimization for T2V Generation Skill Distillation T2V Generation

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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.