AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning
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 engin...
Bing-Xiang He, Peng Sun, Shi-Hao Peng et al.
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