FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents
Fine-tuning large language models for vertical domains remains labor-intensive, requiring practitioners to curate data, configure training, and iteratively diagnose model behavior. Despite growing interest in autonomous machine learning and language agents, end-to-end LLM fine-tuning has not been systematically studied as an interactive agent task. We introduce FT-Dojo, an interactive benchmark environment for autonomous LLM fine-tuning, comprising 13 tasks across 5 domains. Rather than a new collection of static datasets, FT-Dojo standardizes a task interface, shared raw-data repository, sandboxed execution environment, structured feedback protocol, and held-out evaluation procedure. We ...
| Authors | Qizheng Li, Yifei Zhang, Xiao Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 March 2026 |
| Last updated at source | 20 May 2026 |
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