arXiv:2603.01712v2 Announce Type: replace-cross Abstract: 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 stand
The development of LLMs is rapidly progressing, making autonomous fine-tuning a critical next step to enhance their practical applicability and address current labor-intensive processes.
This development can significantly accelerate the customisation and deployment of LLMs for specialized tasks, reducing costs and increasing efficiency for businesses and researchers.
The process of fine-tuning large language models moves from a highly human-intensive task to one that can be largely automated by AI agents.
- · AI developers
- · Businesses using LLMs
- · AI platform providers
- · SaaS companies
- · Manual data curaters
- · Traditional ML operations teams
FT-Dojo enables more efficient and scalable deployment of specialized LLMs across various industries.
The proliferation of autonomously fine-tuned LLMs could lead to AI agents taking over more complex and domain-specific tasks.
Increased automation of LLM fine-tuning may accelerate the development of truly autonomous AI systems capable of self-improvement.
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Read at arXiv cs.LG