Panoramic Analysis of Post-Training Alignment Technologies for Large Models: A Practical Guide from Full Fine-Tuning to DPO Preference Optimization

Introduction / Main Floor: Panoramic Analysis of Post-Training Alignment Technologies for Large Models: A Practical Guide from Full Fine-Tuning to DPO Preference Optimization

An in-depth exploration of three core technologies in the post-training phase of large language models: full fine-tuning (FFT) for language modeling, parameter-efficient fine-tuning (PEFT) for skill acquisition, and direct preference optimization (DPO) for behavior alignment, helping developers understand the trade-offs and applicable scenarios of each technology.