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<a href="/blog/2026/03/paper-qa-eval.html" class="post-title-link" itemprop="url">《FQ-Eval Building Evaluation Dataset for User-centered Follow-up Question Generation》</a>
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<time title="创建时间：2026-03-04 16:20:00 / 修改时间：14:11:49" itemprop="dateCreated datePublished" datetime="2026-03-04T16:20:00+08:00">2026-03-04</time>
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<p>FQ-Eval 这是一篇关于对话中的 Follow ups questions 的论文。</p>
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<a href="/blog/2025/12/llm-dapo.html" class="post-title-link" itemprop="url">大模型训练方法：DAPO</a>
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<span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/LLM/" itemprop="url" rel="index"><span itemprop="name">LLM</span></a></span>
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<span>11 分钟</span>
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<p>DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization) 作为大模型强化学习训练的新兴算法，通过四项核心改进有效解决了GRPO在长序列优化中的痛点问题，在数学推理等复杂任务中取得了显著的性能提升。</p>
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<time title="创建时间：2025-11-18 16:20:00" itemprop="dateCreated datePublished" datetime="2025-11-18T16:20:00+08:00">2025-11-18</time>
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<p>GSPO（Group Sequence Policy Optimization，群组序列策略优化）是对 GRPO 的重要改进。通过将优化粒度从 <strong>token 级</strong> 提升到 <strong>序列级</strong>，GSPO 从根本上解决了 GRPO 在处理长文本、MoE 模型时的训练不稳定问题，同时保持了轻量化的优势（无需 Critic 模型）。</p>
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<p>在现代 AI 应用开发中，让大语言模型（LLM）生成结构化的 JSON 数据是一个关键需求。无论是构建 API 服务、数据处理流水线，还是与现有系统集成，结构化输出都是必不可少的。本文将深入探讨多种让 LLM 生成规范 JSON 的方法，从基础技巧到高级工程实践。</p>
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<a href="/blog/2025/09/how-to-build-multi-agents.html" class="post-title-link" itemprop="url">翻译《如何构建多智能体研究系统：Anthropic 的工程实践》</a>
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<p>最近在看打造 Agent 相关的研究，发现 Anthropic 他们的一篇文章写的特别好，有很多工程实践经验值得参考。虽然没有披露更多细节，但是也指出了很多方向。以下基本是原文翻译。</p>
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<p>RAG（检索增强生成）系统的核心在于能否准确、高效地检索到与用户查询最相关的文档片段。检索质量的好坏直接决定了最终生成结果的准确性和可靠性。本文将深入探讨 RAG 系统中检索优化的关键策略和最佳实践。</p>
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