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
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
Representation fine-tuning (ReFT): A Powerful Parameter . . . In the paper [3], researchers propose Representation Finetuning (ReFT) approach, which operates on a frozen base model and learn task-specific interventions on hidden representations This
ReFT: Enhancing LLMs with reinforcement fine-tuning Reinforcement fine-tuning is a way to improve large language models by training them with a reward-based process These “frontier models” are already capable of many tasks—like translation, assistance, and coding—but there’s ongoing research into how to fine-tune them efficiently
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
[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
Paper Explained — REFT: Reasoning with REinforced Fine-Tuning In the ever-evolving landscape of artificial intelligence, a breakthrough approach called Reinforced Fine-Tuning (ReFT) is changing how Large Language Models (LLMs) learn to solve mathematical
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