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Southern University of Science and Technology
- Shenzhen, China
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10:26
(UTC -07:00) - https://keyuzhu19.github.io/
- @Kit_Key_
- in/keyu-zhu-7237433b2
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A curated collection of papers, technical reports, frameworks, and tools for on-policy distillation (OPD) of large language models
🚀 An open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.
Implementation of RoboTTT proposed by Yunfan Jiang et al. of Stanford and Nvidia
Official code for AdaJEPA: An Adaptive Latent World Model
Behavior Prompting Policy: Demonstrations as Prompts for Manipulation
ICLR 2026 paper - Towards a Foundation Model for Crowdsourced Label Aggregation
ResearchStudio: Our AI co-author, from research problem to final publication.
TACO: TActile World Model as a Self-COrrector for Scalable VLA Post-Training
Code for World Pilot: Steering Vision-Language-Action Models with World-Action Priors.
[AAAI 2025 Oral] FlowPolicy: Enabling Fast and Robust 3D Flow-based Policy via Consistency Flow Matching for Robot Manipulation
Official implementation of the paper "Conditioning Matters: Training Diffusion Policies is Faster Than You Think".
Behavior Prompting Policy: Demonstrations as Prompts for Manipulation
Code for the project "MegaSaM: Accurate, Fast and Robust Structure and Motion from Casual Dynamic Videos"
EgoVerse: Egocentric Data for Robot Learning from Around the World
🔥 DanceOPD: On-Policy Generative Field Distillation
[ICML 2026] ResVLA: From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
Skills for Real Engineers. Straight from my .agents directory.
Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.
[ECCV 2026] VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
A collection of agent skills for Claude Code, Cursor, and other AI coding tools
[ICLR 2026 Oral] DiffusionNFT: Online Diffusion Reinforcement with Forward Process
Official PyTorch Implementation of "Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching"