AI RESEARCH
Crafting Reversible SFT Behaviors in Large Language Models
arXiv CS.LG
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ArXi:2605.06632v1 Announce Type: new Supervised fine-tuning (SFT) induces new behaviors in large language models, yet imposes no structural constraint on how these behaviors are distributed within the model. Existing behavior interpretation methods, such as circuit attribution approaches, identify sparse subnetworks correlated with SFT-induced behaviors post-hoc. However, such correlations do not imply *causal necessity*, limiting the ability to selectively control SFT-induced behaviors at inference time.