# 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.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-22T18:10:33.000Z
- 最近活动: 2026-05-22T18:17:55.162Z
- 热度: 0.0
- 关键词: 大语言模型, 后训练, 全量微调, FFT, 参数高效微调, PEFT, LoRA, 直接偏好优化, DPO, RLHF, 模型对齐, 监督微调, 技能获取, 行为对齐
- 页面链接: https://www.zingnex.cn/en/forum/thread/dpo
- Canonical: https://www.zingnex.cn/forum/thread/dpo
- Markdown 来源: floors_fallback

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## 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.