A single agent only reweights SFT data: splitting execution, four analyses and synthesis lifts IFEval to 63.33 by round six
Language model agents are increasingly handed the post-training stack, yet a single agent converges on supervised fine-tuning and spends its effort reweighting data categories. AutoSFT splits the loop into an executor, four analysis subagents that read each round from training data, failures, training signals and history, and a synthesis subagent that writes new examples for undersupplied, verifier-checkable capabilities. Every round retrains from the same base checkpoint, so each component is attributable: analysis reaches 63.33 on IFEval at round six, above the single agent's best of 62.35 across 50 rounds.