Aligning Large Language Models with Human Feedback: Mathematical Foundations and Algorithm Design

Overview

arXiv:2610.31862v1 Announce Type: new Abstract: This article provides an introduction to the mathematical foundations and algorithmic frameworks used to align Large Language Models (LLMs) with human intentions, preferences, and values. We discuss standard alignment techniques, such as fine-tuning (SFT), reinforcement learning with human feedback (RLHF), and direct preference optimization (DPO). We also explore the theoretical underpinnings of learning from human preferences, drawing connections

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