--- license: apache-2.0 language: - en base_model: - Qwen/Qwen2.5-32B-Instruct --- # AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials
[\[🏠Homepage\]](https://agenttrek.github.io/) [\[💻Code\]](https://github.com/xlang-ai/AgentTrek) [\[📝Paper\]](https://arxiv.org/abs/2412.09605) [\[🤗Models\]](https://huggingface.co/xlangai/AgentTrek-1.0-32B)[\[🤗Data\]]()
## Overview of Pipeline ![image/png](https://cdn-uploads.huggingface.co/production/uploads/669ca7e678115e16bdfc9bfc/ULRzaG6fLCdR7ThkPyKwg.png) AgentTrek is a cost-efficient and scalable framework that synthesizes high-quality agent trajectories by guiding replay with web tutorials. These collected trajectories significantly enhance agent performance. ## Quick Start **AgentTrek-1.0-32B** is a web agent model finetuned from [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/669ca7e678115e16bdfc9bfc/ET8fq3iG-1N-lmy_ltNuH.png) - For metrics, refers to [Browsergym Leaderboard](https://huggingface.co/spaces/ServiceNow/browsergym-leaderboard) - For evaluation, refers to [Evaluation Script](https://github.com/xlang-ai/AgentTrek) - For training dataset, refers to [Training Dataset]() ## Citation ```bibtex @article{xu2024agenttrek, author = {Yiheng Xu and Dunjie Lu and Zhennan Shen and Junli Wang and Zekun Wang and Yuchen Mao and Caiming Xiong and Tao Yu}, title = {AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials}, year={2024}, eprint={2412.09605}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.09605} } ```