🤖 What happens when you hand an agent the full SFT data-research task?
It only mixes ratios. No repaired samples, no new responses, no synthetic data — a word cloud full of "weights" and "proportions", and almost no score gain over dozens of rounds.
AutoSFT (THUNLP × BUPT × ModelBest × UIUC × CUHK) splits the data-research lifecycle into roles: a dedicated Executor, 4 Analysis Subagents (training data / failure cases / learning dynamics / research history), a Synthesis writer with a verifier, and a meta agent that picks the next recipe.
1️⃣ Single-agent baseline: 33 rounds, only Mixture; context hit 94.7%, compressed 10 times; Executor cuts compression 10→5 and shell events 58%→29%.
2️⃣ AutoSFT: all 6 action families appear across 30 rounds; IFEval round 6 = 63.33, beating the baseline's best over 50 rounds; +6.5 at the same budget; round 30 = 67.10.
3️⃣ Synthesis alone: IFEval +3.4, IFBench +2.9; MBPP+ coding +3.7.
The takeaway: automating data research may depend more on covering diverse data actions than on a stronger single action.
📄 Paper: https://arxiv.org/abs/2610.22194
💻 Code: https://github.com/Thinking-Space/AutoSFT
#AIAgent #SFT #LLM #DataCentricAI #PostTraining #RSI #AgenticAI #THUNLP #AutoSFT #FineTuning