<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>DPO on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/dpo/</link><description>Recent content in DPO on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Thu, 16 Jul 2026 11:05:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/dpo/index.xml" rel="self" type="application/rss+xml"/><item><title>AI安全对齐技术栈：从RLHF到Constitutional AI</title><link>https://guijiagi.com/posts/b1-fd3b570e/</link><pubDate>Thu, 16 Jul 2026 11:05:00 +0800</pubDate><guid>https://guijiagi.com/posts/b1-fd3b570e/</guid><description>梳理AI对齐的主流技术路线，从RLHF到DPO再到宪法AI的演进逻辑与实现要点</description></item><item><title>AI安全对齐技术栈：从RLHF到Constitutional AI</title><link>https://guijiagi.com/posts/b2-fd3b570e/</link><pubDate>Thu, 16 Jul 2026 10:05:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-fd3b570e/</guid><description>全面梳理大模型安全对齐技术体系，涵盖RLHF、DPO、Constitutional AI及红队测试实践</description></item><item><title>从数据标注到RLHF：对齐全流程实践</title><link>https://guijiagi.com/posts/article-85/</link><pubDate>Mon, 13 Jul 2026 07:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-85/</guid><description>系统梳理大模型对齐的完整流程，从数据标注到SFT到RLHF再到DPO的工程实践</description></item><item><title>大模型的安全对齐技术全景</title><link>https://guijiagi.com/posts/article-63/</link><pubDate>Mon, 13 Jul 2026 03:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-63/</guid><description>系统梳理大模型安全对齐技术栈，从RLHF到Constitutional AI，从训练阶段到推理阶段的全链路安全方案</description></item><item><title>强化学习RLHF的替代方案：DPO全面解析</title><link>https://guijiagi.com/posts/article-10/</link><pubDate>Sun, 12 Jul 2026 18:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-10/</guid><description>深入解析Direct Preference Optimization的数学原理和实践优势，探讨它如何简化RLHF流程</description></item><item><title>强化学习RLHF的替代方案：DPO全面解析</title><link>https://guijiagi.com/posts/b2-c244c3f2/</link><pubDate>Sun, 12 Jul 2026 18:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-c244c3f2/</guid><description>深入解析Direct Preference Optimization的数学原理和实践优势，探讨它如何简化RLHF流程</description></item><item><title>DPO训练实践指南：直接偏好优化的工程落地</title><link>https://guijiagi.com/posts/dpo-training-practice/</link><pubDate>Thu, 02 Jul 2026 10:43:00 +0800</pubDate><guid>https://guijiagi.com/posts/dpo-training-practice/</guid><description>系统介绍DPO（Direct Preference Optimization）的训练原理、数据准备与工程实践</description></item><item><title>强化学习对齐 2026：从 RLHF 到 DPO 再到 ORPO</title><link>https://guijiagi.com/posts/alignment-evolution-rlhf-to-orpo/</link><pubDate>Tue, 30 Jun 2026 17:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/alignment-evolution-rlhf-to-orpo/</guid><description>大模型对齐技术演进：RLHF、DPO、ORPO、GRPO等核心算法原理、实现要点与2026年最新进展</description></item><item><title>DPO偏好对齐训练：数据工程与超参调优</title><link>https://guijiagi.com/posts/dpo-preference-alignment-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/dpo-preference-alignment-guide/</guid><description>DPO已成为偏好对齐的主流方案，本文详解DPO的数据构建、训练配置与超参调优全流程</description></item><item><title>RLHF替代方案2026：DPO、GRPO、SimPO技术对比</title><link>https://guijiagi.com/posts/rlhf-alternatives-2026-dpo-grpo-simpo/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/rlhf-alternatives-2026-dpo-grpo-simpo/</guid><description>深入对比2026年主流的RLHF替代方案：DPO、GRPO、SimPO的技术原理、优缺点和适用场景</description></item><item><title>RLHF替代方案2026：DPO、GRPO、SimPO技术对比</title><link>https://guijiagi.com/posts/rlhf-alternatives-2026/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/rlhf-alternatives-2026/</guid><description>全面对比2026年主流RLHF替代方案，涵盖DPO、GRPO、SimPO等对齐技术的原理、优缺点和适用场景</description></item><item><title>大模型训练流程：预训练/SFT/RLHF/DPO 全链路</title><link>https://guijiagi.com/posts/llm-training-pipeline-pretrain-sft-rlhf-dpo/</link><pubDate>Sun, 28 Jun 2026 11:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-training-pipeline-pretrain-sft-rlhf-dpo/</guid><description>系统解析大模型训练的四个阶段：预训练、监督微调、RLHF、DPO的原理、流程与2026年最佳实践</description></item><item><title>AI 对齐 2026：从 RLHF 到 Constitutional AI 的最新进展</title><link>https://guijiagi.com/posts/ai-alignment-2026-rlhf-constitutional/</link><pubDate>Sun, 28 Jun 2026 10:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-alignment-2026-rlhf-constitutional/</guid><description>2026年AI对齐技术全景：RLHF、Constitutional AI、DPO及最新对齐方法的深度解析</description></item><item><title>DPO 训练实践：偏好对齐的数据工程</title><link>https://guijiagi.com/posts/dpo-training-practice-preference-alignment-data-engineering/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/dpo-training-practice-preference-alignment-data-engineering/</guid><description>深入解析 DPO（Direct Preference Optimization）训练流程，涵盖偏好数据构建、训练配置和效果评估</description></item><item><title>RLHF vs DPO vs GRPO：三种对齐算法深度对比</title><link>https://guijiagi.com/posts/rlhf-dpo-grpo/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rlhf-dpo-grpo/</guid><description>从 RLHF 的 PPO 到 DPO 的无奖励模型，再到 GRPO 的群组相对策略优化，深度解析三种主流 LLM 对齐算法的原理与优劣。</description></item><item><title>DPO vs RLHF：偏好对齐的两条路线</title><link>https://guijiagi.com/posts/dpo-vs-rlhf/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/dpo-vs-rlhf/</guid><description>深入对比 RLHF 三阶段流程与 DPO 直接偏好优化的原理、优劣势和工程实践</description></item><item><title>AI 对齐技术全景：从 RLHF 到 Constitutional AI</title><link>https://guijiagi.com/posts/ai-alignment-techniques/</link><pubDate>Wed, 24 Jun 2026 11:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-alignment-techniques/</guid><description>系统梳理 AI 对齐的技术路径、方法对比和实践选择</description></item></channel></rss>