116 posts in total. Keep on posting.

2026

• 04-12 EN

  Voyager: An Open-Ended Embodied Agent with Large Language Models — Deep Technical Review

  SmoothQuant migrates activation outliers into weights so large language models can run accurate W8A8 inference efficiently on real hardware.

• 04-11 中

  LATS（Language Agent Tree Search）：把推理、行动、规划统一到同一个语言模型代理框架里 — 深度阅读笔记

  1. 为什么这篇论文值得你认真读 如果让我用一句最朴素的话解释这篇论文，我会这么说： LATS 的目标，是让语言模型代理别再“想到啥就一路冲到底”，而是像一个稳健的研究员一样，先比较几条路线，再选更靠谱的一条执行。 很多现有代理方法看起来“会思考”，但在复杂任务里经常出现同一种失败模式： 第一步选错方向； 后续每一步都建立在错误前提上； 中间即使拿到外部反馈，也不会真正改策略； 最后输出一个“局部看起来合理、全局其实失败”的结果。 人类做难题通常不是这样。我们更像是： 先想几种可能路径； 试一条； 发现不对就回退； 再换一条； 对比后再做最终决定。 这篇论文的核心贡献，不是提出一个花哨提示词，而是给出一个统一框架，把三件事放到同一个闭环里： Reasoning（推理）：模型内部语言思考； Acting（行动）：与外部环境交互（检索、执行、点击等）； Planning（规划）：用树搜索比较多条未来路径。 更重要的是，它不是只在一个 benchmark 上“偶然涨分”。论文在多个不同类型任务上都展示了收益： 多跳问答（HotPotQA） 代码生成（HumanEval、MBPP） 网页购物决策（WebShop） 数学推理（Game of 24） 这说明它更像一个“通用代理架构设计”，而不只是某道题的小技巧。

• 04-11 EN

  Language Agent Tree Search (LATS): Unifying Reasoning, Acting, and Planning in Language Models — Deep Technical Review

  1. Why this paper deserves a careful read If I explain this paper in one sentence to a reader who has never built an AI agent before, I would say this: LATS tries to make language-model agents less impulsive and more deliberate by letting them search over multiple possible action paths instead of committing to the first plausible answer. That may sound simple, but it addresses one of the biggest weaknesses of many LM agents. A lot of agent systems look smart for the first two or three steps and then fail because they: choose a bad early action, never seriously consider alternatives, get trapped in their own earlier mistake, or cannot make good use of external feedback once they receive it. Human beings do not usually solve hard tasks by blurting out one chain of thought and marching forward forever. We often: consider multiple options, test one path, notice that it is going badly, backtrack, compare another path, and only then commit. This paper asks a very important question: Can we make LM agents behave a little more like that without retraining the model from scratch? The authors’ answer is yes, to a meaningful extent, by combining three capabilities that earlier work usually treated separately: reasoning — internal thinking in language, acting — interacting with tools or environments, planning — using search to compare candidate futures before fully committing. The reason I think this paper matters is not that it proves agentic search is fully solved. It absolutely does not. The reason it matters is that it gives a concrete, general framework that works across very different tasks: multi-hop question answering, programming, web shopping/navigation, and even mathematical reasoning. That breadth is rare. Many agent papers look good on one benchmark and then collapse outside that niche. LATS is interesting because it is trying to be a general agent framework, not just a benchmark-specific trick.

• 04-10 中

  SVD-LLM：面向大语言模型压缩的“截断感知”奇异值分解方法 — 深度阅读笔记

  1. 这篇论文为什么值得认真读 我先用一句话给结论： SVD-LLM 把 SVD 压缩里最关键的“截断决策”从经验做法，升级成和真实压缩损失直接对齐的数学机制；再通过顺序式低秩参数更新，把高压缩率下的质量崩塌拉回来。 为什么这是大事？因为现实里的 LLM 部署痛点非常具体： 权重太大，显存压力爆炸； 线上推理成本高； 压缩到中高比例时，很多方法会“看起来压了，实际不可用”。 SVD 路线一直很诱人：硬件友好、结构规整、并且可同时影响权重与推理内存。但过去的 SVD 方案在高压缩率下经常掉得太厉害。SVD-LLM 的意义就在于，它不是“调了一点超参”，而是把根因给修了。

• 04-10 EN

  SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression — Deep Technical Review

  1. Why this paper matters If I must explain this paper to a complete beginner in one sentence: SVD-LLM makes low-rank compression for large language models much more reliable by fixing two core problems in older SVD methods: wrong truncation guidance and no post-truncation recovery update. This matters because today’s LLM deployment pain is not abstract: model weights are huge, memory budgets are real, latency and hardware costs are painful, and many “easy compression” methods collapse at high compression ratios. SVD-based compression has always looked attractive because it is hardware-friendly and can reduce both parameter memory and runtime footprint, but prior SVD compression pipelines (for LLMs) often become unstable or degrade sharply when compression ratio gets aggressive. SVD-LLM’s contribution is to make SVD truncation mathematically aligned with loss and then recover quality through a sequential low-rank parameter update. In experiments, that combination is exactly where the big gap appears.

• 04-09 中

  DistServe：通过 Prefill/Decoding 解耦实现面向 Goodput 的大模型服务优化 — 深度阅读笔记

  1. 这篇论文为什么值得认真读 先给一句结论： DistServe 的价值在于：它把大模型服务里最痛的“延迟-成本矛盾”从调度层面硬扛，升级成“架构层面解耦”，然后再用算法做资源与并行策略的联合优化。 很多服务系统论文会给你“吞吐更高”的漂亮数字，但实际线上产品最在意的是： 用户是否足够快看到首个 token（TTFT）？ 后续生成是否足够流畅（TPOT）？ 在满足服务质量（SLO）的前提下，每块 GPU 的产出值不值？ DistServe 不是只追求 token/s，而是明确优化 goodput（满足 SLO 约束下的单位 GPU 可服务请求率）。这点非常“工程真相”。 论文报告的核心收益是： 相比主流方案，最高 7.4× 请求率提升； 或在同样请求率下，实现 12.6× 更严格 SLO； 且 >90% 请求满足延迟约束。 这是“性能数字 + 用户体验 + 成本效率”三者同时进步，而不是单指标提升。

• 04-09 EN

  DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving — Deep Technical Review

  1. Why This Paper Matters If I explain this paper in one sentence: DistServe improves LLM serving under latency SLOs by separating prefill and decoding onto different GPU pools, then jointly optimizing their resources and placement to maximize goodput per GPU. This sounds simple, but it addresses one of the deepest frustrations in real-world LLM systems engineering: product wants both fast first response and smooth generation, infrastructure wants high utilization and low cost, and existing colocated serving designs often force a painful compromise. The paper makes this problem concrete, quantifies why the compromise happens, proposes a practical architecture, and validates it with strong end-to-end gains: up to 7.4× higher request rate at target SLO attainment, or 12.6× tighter SLOs at fixed rate, while keeping latency constraints satisfied for >90% of requests. That combination (clear diagnosis + design + measurable gains + deployment details) is why this is a serious ML systems paper.

• 04-08 EN

  SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models — In-Depth Technical Review

  1. Why This Paper Matters If you only remember one sentence from this review, I want it to be this: SmoothQuant is important because it turns a seemingly annoying numerical issue—activation outliers—into a clean systems trick that real hardware can actually use. Large language models are expensive for two reasons: they store a huge amount of weights, and they repeatedly move those weights and activations through matrix multiplications. That means memory footprint, memory bandwidth, and integer-kernel friendliness are not side details. They are central engineering constraints.

• 04-08 中

  SmoothQuant：大型语言模型的精准高效训练后量化 — 深度阅读笔记

  SmoothQuant 通过把激活值中的离群难度迁移到权重中，让大模型能够更稳定地实现高效 W8A8 推理。

• 04-07 EN

  ORPO: Monolithic Preference Optimization without Reference Model — In-Depth Technical Review

  ORPO merges supervised fine-tuning and preference optimization into one objective, removing the need for a separate reference model while keeping strong alignment performance.

• 04-07 中

  ORPO：不用参考模型的一体化偏好优化 — 深度阅读笔记

  ORPO 将监督微调和偏好优化合并到一个目标中，在不依赖参考模型的情况下实现更简单的对齐训练。

• 04-04 EN

  Switch Transformers: Scaling to Trillion-Parameter Sparse Models — In-Depth Technical Review

  Switch Transformer routes each token to a single expert, making trillion-parameter sparse models practical with major training-speed gains and simpler MoE scaling.