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- 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
- ReFT: Representation Finetuning for Language Models
ReFT represents a novel approach to parameter-efficient, powerful, and interpretable fine-tuning of language models
- 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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- ReFT: Representation Finetuning for Language Models - Medium
Learn about Representation Finetuning (ReFT) by Stanford University, a method to fine-tune large language models (LLMs) efficiently
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