ZhengxuanWu∗† Aryaman Arora∗† Zheng Wang† Atticus Geiger‡Dan Jurafsky† Christopher D. Manning† Christopher Potts††Stanford University ‡Pr(Ai)2R Group{wuzhengx,aryamana,peterwz,atticusg,jurafsky,manning,cgpotts}@stanford.eduAbstractParameter-efficient fine-tuning (PEFT) methods seek to adapt large models via updates to a small number of weights. However, much prior interpretability work has shown that representations encode rich semantic information, suggesting that editing representations might be a more powerful alternative. Here, we pursue this hypothesis by developing a family of Representation Finetuning (ReFT) methods. ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations. We define a strong instance of the ReFT family, Low-rank Linear Subspace ReFT (LoReFT). LoReFT is a drop-in replacement for existing PEFTs and learns interventions that are 10×–50× more parameter-efficient than prior state-of-the-art PEFTs. We showcase LoReFT on eight commonsense reasoning tasks, four arithmetic reasoning tasks, Alpaca-Eval v1.0, and GLUE. In all these evaluations, LoReFT delivers the best balance of efficiency and performance, and almost always outperforms state-of-the-art PEFTs. We release a genericReFT training library publicly at https://github.com/stanfordnlp/pyreft.