<?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>对齐 on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/%E5%AF%B9%E9%BD%90/</link><description>Recent content in 对齐 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/%E5%AF%B9%E9%BD%90/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>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>大模型幻觉问题：成因分析与缓解策略</title><link>https://guijiagi.com/posts/article-21/</link><pubDate>Sun, 12 Jul 2026 20:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-21/</guid><description>深入剖析大模型幻觉的成因机制，从训练数据到推理过程的系统性缓解方案</description></item><item><title>大模型幻觉问题：成因分析与缓解策略</title><link>https://guijiagi.com/posts/b2-2909bef4/</link><pubDate>Sun, 12 Jul 2026 20:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-2909bef4/</guid><description>深入剖析大模型幻觉的成因机制，从训练数据到推理过程的系统性缓解方案</description></item><item><title>2026年AI安全十大趋势预测</title><link>https://guijiagi.com/posts/article-14/</link><pubDate>Sun, 12 Jul 2026 19:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-14/</guid><description>从对抗攻击到对齐安全，全面预测2026年下半年AI安全领域的关键趋势和新兴威胁</description></item><item><title>2026年AI安全十大趋势预测</title><link>https://guijiagi.com/posts/b2-5e1b07ab/</link><pubDate>Sun, 12 Jul 2026 19:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-5e1b07ab/</guid><description>从对抗攻击到对齐安全，全面预测2026年下半年AI安全领域的关键趋势和新兴威胁</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>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>超级对齐2026：控制超越人类智能的AI</title><link>https://guijiagi.com/posts/superalignment-2026/</link><pubDate>Tue, 30 Jun 2026 10:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/superalignment-2026/</guid><description>2026年超级对齐技术的最新进展，从可扩展监督到机制可解释性，探讨如何控制超越人类智能的AI系统</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>大模型对齐技术：从RLHF到Constitutional AI的完整路径</title><link>https://guijiagi.com/posts/llm-alignment-constitutional-ai/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-alignment-constitutional-ai/</guid><description>系统梳理大模型对齐技术的发展脉络，从RLHF到Constitutional AI再到2026年最新的对齐方案</description></item><item><title>大模型幻觉问题：根因分析与缓解技术全景</title><link>https://guijiagi.com/posts/llm-hallucination-mitigation/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-hallucination-mitigation/</guid><description>深入分析大模型幻觉问题的根因，系统介绍从训练到推理全链路的缓解技术</description></item><item><title>大模型宪法AI方法实践</title><link>https://guijiagi.com/posts/constitutional-ai-practice/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/constitutional-ai-practice/</guid><description>深入解析宪法AI（Constitutional AI）的核心原理、实施步骤与工程实践经验</description></item><item><title>强化学习RLHF技术原理详解</title><link>https://guijiagi.com/posts/rlhf-technique-deep-dive/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rlhf-technique-deep-dive/</guid><description>强化学习RLHF技术原理详解</description></item><item><title>AI 智能体伦理框架：从原则到实践</title><link>https://guijiagi.com/posts/ai-agent-ethics-framework/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-agent-ethics-framework/</guid><description>系统构建 AI 智能体伦理治理框架，从抽象原则到工程实现，覆盖价值观对齐、安全护栏、责任归属和审计追踪全链路。</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>AI 对齐 2026：从 RLHF 到 Constitutional AI 的演进</title><link>https://guijiagi.com/posts/ai-alignment-2026/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-alignment-2026/</guid><description>深度解析 AI 对齐技术的演进路径，从 RLHF 的局限到 Constitutional AI、DPO/KTO 及可扩展监督的最新进展。</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>LLM 安全评估指南：Toxicity/Bias/Jailbreak 全维度</title><link>https://guijiagi.com/posts/llm-safety-eval-guide/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-safety-eval-guide/</guid><description>LLM 安全评估全维度指南：毒性检测、偏见评估、越狱攻击测试与对齐评测</description></item><item><title>AGI 安全研究前沿：当我们造出超人类 AI</title><link>https://guijiagi.com/posts/agi-safety-research/</link><pubDate>Wed, 24 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agi-safety-research/</guid><description>系统梳理 AGI 安全研究的前沿方向，涵盖对齐问题、可扩展监督、机械可解释性和停机问题</description></item><item><title>指令微调指南：从 SFT 到 DPO</title><link>https://guijiagi.com/posts/instruction-tuning-guide/</link><pubDate>Wed, 24 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/instruction-tuning-guide/</guid><description>系统讲解 LLM 指令微调全流程，从 SFT 数据构造到 DPO 直接偏好优化与 RLHF 对比</description></item><item><title>AGI 安全全景图：从对齐问题到可控性设计的思考</title><link>https://guijiagi.com/posts/agi-safety-landscape/</link><pubDate>Tue, 23 Jun 2026 15:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/agi-safety-landscape/</guid><description>系统梳理 AGI 安全的核心问题域、技术路线和治理框架</description></item></channel></rss>