ETO-SFT: Expert Trajectories for LLM Agent Training via Trial and Error

Expert trajectories for the ETO (Exploration-based Trajectory Optimization) framework, a method for training large language model agents through iterative trial and error. The dataset was created by authors Yifan Song, Da Yin, Xiang Yue, Jie Huang, Sujian Li, and Bill Yuchen Lin, with a paper published on arXiv in March 2024. It was uploaded to Hugging Face by the user 'agent-eto' on April 9, 2024.

Use Cases

• Training LLM agents via imitation learning based on expert trajectories.
• Benchmarking exploration and trial-and-error algorithms for agent learning.
• Analyzing failure trajectories to improve agent policy updates.
• Studying the ETO framework's methodology for trajectory optimization.

Strengths

• Dataset is associated with a specific, named research framework (ETO) and a peer-reviewed arXiv paper (2403.02502).
• The dataset page provides direct links to the project's homepage, GitHub repository, and research paper for context.
• The dataset was uploaded on April 9, 2024, indicating recent availability.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Hugging Face user 'agent-eto', associated with the research paper 'Exploration-Based Trajectory Optimization for LLM Agents'.

Collection Method

Likely contains trajectories generated by or for expert agents within the ETO learning framework.

Freshness

Last updated 2024-04-09 07:35:29