ReFT: Representation Finetuning for Language Models 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
[2401. 08967] ReFT: Reasoning with Reinforced Fine-Tuning To address this issue, we propose a simple yet effective approach called Reinforced Fine-Tuning (ReFT) to enhance the generalizability of learning LLMs for reasoning, with math problem-solving as an example
GitHub - stanfordnlp pyreft: Stanford NLP Python library for . . . ReFT is different: (1) ReFT selects timesteps to intervene on; and (2) ReFT targets representations instead of weights To help you understand these differences, let's consider these cases: Learning LoRA weights on o_proj Learning ReFT interventons that apply to o_proj across all timesteps
ReFT: Reasoning with Reinforced Fine-Tuning - ACL Anthology To address this issue, we propose a simple yet effective approach called Reinforced Fine-Tuning (ReFT) to enhance the generalizability of learning LLMs for reasoning, with math problem-solving as an example
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ReFT: Representation Finetuning for Language Models Representation Finetuning (ReFT) methods, exemplified by Low-rank Linear Subspace ReFT (LoReFT), achieve high efficiency and performance by adapting representations in frozen base models, outperforming state-of-the-art Parameter-efficient Fine-tuning (PEFT) methods