ReFT: Representation Finetuning for Language Models 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), and we identify an ablation of this method that trades some performance for increased efficiency
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
Paper page - ReFT: Reasoning with Reinforced Fine-Tuning Reinforced Fine-Tuning (ReFT) improves the generalizability of large language models in reasoning tasks like math problem-solving by using reinforcement learning to learn from multiple reasoning paths
[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
ReFT: Representation Finetuning for Language Models 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), and we identify an ablation of this method that trades some performance for increased eficiency
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