The Mechanics of Alignment: A Comprehensive Analysis of RLHF, Direct Preference Optimization, and Parameter-Efficient Architectures in Large Language Models

1. Introduction: The Post-Training Paradigm and the Alignment Challenge The contemporary landscape of artificial intelligence has been irrevocably altered by the emergence of Large Language Models (LLMs) trained on datasets Read More …

The Mechanics of Alignment: A Comprehensive Analysis of RLHF, Direct Preference Optimization, and Parameter-Efficient Architectures in Large Language Models

1. Introduction: The Post-Training Paradigm and the Alignment Challenge The contemporary landscape of artificial intelligence has been irrevocably altered by the emergence of Large Language Models (LLMs) trained on datasets Read More …

Parameter-Efficient Adaptation of Large Language Models: A Technical Deep Dive into LoRA and QLoRA

The Imperative for Efficiency in Model Adaptation The advent of large language models (LLMs) represents a paradigm shift in artificial intelligence, with foundation models pre-trained on vast datasets demonstrating remarkable Read More …