AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning
arXiv:2610.22194v1 Announce Type: new Abstract: 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. 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. We design a data action space for these changes and as…