AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning

Key points

• Language model agents increasingly automate post-training, but when given the full training stack they converge on supervised fine-tuning and spend most effort revising training data.1
• Existing systems give the agent one kind of control, selecting rows from a fixed pool or calling a generator, whereas a human data engineer adjusts proportions, prunes, repairs, replaces and synthesizes the data.1
• We first run this loop with a single agent, and it reaches only one action, adjusting category proportions, while its context fills with execution output and is discarded mid-search.1
• Our findings suggest that automating SFT data research depends more on covering diverse data actions than on a stronger single action.1

Sources (2)

• [1]AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning
  Hugging Face Daily Papers · Oct 15, 12:00 AM

  We therefore introduce AutoSFT, a multi-agent system where a meta agent separates execution from analysis, gathers proposals from independent angles, and calls a synthesis subagent for capabilities the pool cannot cover, with the model, the trainer, and the budget fixed within each comparison.

  Language model agents increasingly automate post-training, but when given the full training stack they converge on supervised fine-tuning and spend most effort revising training data.

• [2]AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 04:44 PM · same content

Extractive summary: sentences quoted from the sources.