Supervised fine-tuning overview

Unsupervised Algorithm for Language Model Fine-Tuning Introduced

The article introduces an unsupervised algorithm, Internal Coherence Maximization (ICM), to fine-tune pretrained language models without ext

Efficient Training Data Reduction Using High-Fidelity Labels and Human Expertise

The article describes a process for achieving significant training data reduction by using a zero- or few-shot initial model (LLM-0) to labe

Supervised Fine-Tuning as Reinforcement Learning: Introducing Importance-Weighted SFT

The article explores the connection between supervised fine-tuning (SFT) of large language models and reinforcement learning (RL), arguing t

Understanding Reinforcement Learning for Model Training, and future directions with GRAPE

Machine Learning Systems

Theoretical Foundations of Deep Learning