2026-10-05

On-Policy or Off-Policy Learning? A Systematic Study of Distillation Dynamics

👍 175

09/28 08:00

On-policy learning has been argued to reduce catastrophic forgetting, produce sparser parameter updates, and improve generalisation. However, existing comparisons between supervised fine-tuning and reinforcement learning vary many factors simultaneously, making the contribution of rollout policy dif

中文介绍 针对「on-policy 学习能否减少灾难性遗忘、产生更稀疏的参数更新并提升泛化」的争议，本文对蒸馏动态做系统性研究。作者指出以往 SFT 与 RL 的对比同时改变多个因素，无法分离 rollout policy 的贡献，因此通过控制变量逐一拆解各要素的影响。

OneStreamer: Unifying Perception, Memory, and Proactive Response in Streaming Video Interaction

👍 165

10/01 08:00

Streaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available. The challenge is to form reusable factual memory without compromising real-time perception. We introduce OneStreamer, which jointly learns query-independent

中文介绍 OneStreamer 面向流式视频交互，解决在尚不知未来任务相关性时保留证据、并在证据充分时主动响应的难题。该方法联合学习与查询无关的事实记忆和实时感知，在不牺牲实时性的前提下构建可复用的记忆，统一感知、记忆与主动响应。

Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs

👍 64

09/29 08:00

Recent multi-agent LLM systems increasingly combine heterogeneous models for specialized agent roles. However, text-based communication requires each receiver to prefill shared context already processed by the sender. Reusing the sender's key-value (KV) cache avoids this redundancy, but prefill-free

中文介绍 异构多智能体 LLM 系统常让不同模型承担专门角色，但基于文本的通信迫使接收方重新 prefill 发送方已处理的共享上下文。本文提出跨模型族的免 prefill KV cache 传输方法，直接复用发送方缓存以消除该冗余计算，降低通信与推理开销。

E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models

👍 60

09/29 08:00

Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically factorized over positions, limiting sample quality in the few-step regime where diffusion's speed advantage over autoregressive decoding matters most

中文介绍 掩码扩散模型每步解掩多个 token，但其反向过程通常按位置分解，削弱了少步采样这一相对自回归解码的速度优势下的生成质量。E-MoE 通过增强的 Mixture-of-Experts 建模位置间依赖，构建非分解式扩散语言模型以提升少步生成质量。

X-Tree: Tokenizing Reusable Experience for Efficient Agent Generalization

👍 59

09/26 08:00

Multi-step agents are trained on flat action streams: SFT and RLVR weight every token uniformly and ignore the sub-procedures that recur across tasks, the hierarchy that lets humans plan top-down from reusable routines. This structure sits unused, and flat training uses each scarce trajectory less f

中文介绍 多步 agent 通常在扁平动作流上训练，SFT 与 RLVR 对所有 token 均匀加权，忽视了跨任务复现的子过程与人类自上而下复用例程的层级结构，造成稀缺轨迹利用率低。X-Tree 将可复用经验 token 化，显式利用层级结构提升泛化与样本效率。

EgoTools: Towards Tool-Centric Reasoning in Real-World Egocentric Videos

👍 57

09/30 08:00

Real-world embodied tasks, from everyday activities to professional procedures, require agents to act under physical constraints while tracking evolving object and task states. Tool use sits at the heart of such tasks, as many everyday and professional activities are tool-mediated. Understanding the

中文介绍 真实具身任务从日常活动到专业流程，要求 agent 在物理约束下行动并跟踪变化的物体与任务状态，其中工具使用居于核心。EgoTools 面向真实第一视角视频中的工具中心推理，构建评测设定以考察 agent 对工具介导活动的理解与决策能力。

Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL

👍 57

09/30 08:00

Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as used in GDPO, preserves reward-specific relative information within rollout groups, but different objectives can still exhibit uneven learning prog

中文介绍 多奖励强化学习让大模型同时满足多种行为目标。GDPO 采用的按奖励归一化虽能在 rollout 组内保留各奖励的相对信息，但不同目标的学习进度仍不均衡，稀疏奖励易被淹没。本文提出密度感知的奖励聚合，按奖励密度调整权重以稳定多目标优化。

InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation

👍 55

10/01 08:00

We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills, improving from its own attempts, and retaining what it learns, without retraining. Our key insight is that a broad controller already holds much of t

中文介绍 本文研究人形机器人移动操作的测试时进化：在不重新训练的前提下，复用控制器已有技能、从自身尝试中改进并保留所学，以完成从未训练过的任务。关键思路是通用控制器已蕴含大部分所需能力，通过在测试时演化奖励程序引导技能重组。

Decentralized Master-Mind: Joint Action Refinement through Iterative Intent Denoising in Multi-Agent Pathfinding

👍 51

09/25 08:00

Decentralized multi-agent path finding (MAPF) with communication requires agents to reach individual goals without collisions under partial observability. Learnable policies trained on expert data provide an effective approach to this problem. However, when several coordinated joint actions are vali

中文介绍 针对部分可观测下的去中心化多智能体路径规划，基于专家数据学习的策略在多个协调联合动作同时有效、意图存在歧义时难以抉择。Decentralized Master-Mind 通过迭代式意图去噪精化联合动作，提升通信协作下的求解成功率。

Fewer Tokens, Better Action: GPT-6 Astra Robot Agents with 14% Higher Success Rate but 65% Fewer Tokens

👍 45

10/01 08:00

Vision language model (VLM) agents can control robots through visual feedback and action primitives, but repeated model invocations and redundant observations incur substantial token overhead. We introduce PyRUA-Lean, an interactive code-execution framework that couples feedback-driven primitive com

中文介绍 VLM 机器人 agent 依赖反复调用模型与冗余观测，token 开销巨大。本文提出 PyRUA-Lean 交互式代码执行框架，将反馈驱动的动作原语组合与执行耦合，在 GPT-6 Astra 上实现成功率提升 14%、token 消耗减少 65%。

Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans

👍 43

09/30 08:00

Furnished floor plans support real-estate visualization, interior design, and architectural workflows, yet automatic furnishing remains challenged by limited real-world data and the need to satisfy interacting geometric and functional constraints. We ask whether professional furnishing knowledge can

中文介绍 户型自动家具布置可服务房产可视化、室内设计与建筑流程，但受限于真实数据稀缺以及相互耦合的几何与功能约束。Architect-Ant 探索将专业布置知识注入自动流程，生成满足多重约束且可编辑的家具布局方案。

Decoding Looped Transformers Better for (Almost) Free

👍 41

10/01 08:00

Looped Transformers achieve parameter efficiency by repeatedly executing a shared block across recurrent loops. Each loop yields an intermediate representation decodable for the same next token, yet standard decoding discards earlier states. Because earlier loops embody less computation, recurrence

中文介绍 循环 Transformer 通过重复执行共享块实现参数高效，每次循环都产生可解码同一 next token 的中间表示，但标准解码会丢弃早期循环状态。本文利用早期循环计算量更小这一特性改进解码策略，以近乎零额外成本提升生成质量。

SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation

👍 35

10/01 08:00

High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective, this design fragments continuous surfaces into many local tokens, inflates generation cost, and often weakens topologi

中文介绍 高分辨率 3D 生成多依赖体素 latent 与「先预测结构、再合成局部几何」的多阶段流程，该设计把连续表面碎片化为大量局部 token，推高生成成本并削弱拓扑一致性。SILSA 提出滑窗切片 latent，在保持拓扑的同时降低生成开销。

CorrGRPO: Correlation-Normalized GRPO for Multi-Reward Learning

👍 30

09/29 08:00

Group Relative Policy Optimization (GRPO) is widely used to train reasoning language models, where it computes advantages by centering and normalizing rewards across rollouts of the same prompt. For multiple rewards, GRPO sums the reward components and normalizes the total reward by its within-group

中文介绍 GRPO 通过在同一 prompt 的多个 rollout 间中心化并归一化奖励来计算优势，被广泛用于推理语言模型训练。面对多奖励时它将各分量求和后按组内标准差归一化总奖励，忽略了奖励间相关性。CorrGRPO 提出相关性归一化以改进多奖励优势估计。

Multimodal Flow: Unified Flow Modeling of Language and Vision in Embedding Spaces

👍 27

09/30 08:00

We present Multimodal Flow, a fully continuous generative model of language and vision. Most unified multimodal models either model both language and quantized images as discrete tokens or combine discrete language prediction with continuous image generation. The former introduces a visual quantizat

中文介绍 主流统一多模态模型要么把语言与量化图像都建模为离散 token，要么将离散语言预测与连续图像生成拼接，前者会引入视觉量化误差。Multimodal Flow 提出在嵌入空间对语言与视觉做完全连续的流建模，实现统一的连续多模态生成。

Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows

👍 26

10/01 08:00

Real-world enterprise data science and analytics workflows require reasoning across dozens of tables, performing statistical analyses, and acting on the results. Established text-to-SQL benchmarks evaluate query generation alone, and audits have found their answer keys frequently wrong. Because real

中文介绍 真实企业数据科学与分析工作流需跨数十张表推理、执行统计分析并据结果行动，而既有 text-to-SQL 基准只评测查询生成，且审计发现其答案键常有错误。Argo-Bench 因此构建企业级工作流基准，评测数据 agent 的端到端分析能力。

Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation

👍 25

10/01 08:00

Video generation models are increasingly being explored as world simulators for embodied planning and learning. To do so effectively, these models must not only generate visually appealing frames, but also predict how environments dynamically evolve when executing goal-directed actions. While evalua

中文介绍 视频生成模型正被用作具身规划与学习的世界模拟器，这要求它不仅生成视觉合理的画面，还要预测执行目标导向动作时环境如何动态演化。Ego2Act 提出第一视角视频生成中目标导向操作能力的评测基准，填补现有评测的空白。

PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loop

👍 23

09/30 08:00

Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities, yet whether they truly capture the physical consistency underlying real-world dynamics remains unclear. Existing benchmark paradigms often suffer from fragmented evaluation, focusing on isolated cognitive stages while

中文介绍 VLM 展现出强多模态推理能力，却未必真正掌握真实世界动态背后的物理一致性，现有基准多聚焦孤立认知阶段、评测碎片化。PhysVista 提出「感知—推理—评估」闭环范式，系统评测 VLM 的物理智能及其对物理规律的理解与运用。

Smaller Models, Better Rejects: Preference Distillation Scaling

👍 21

09/30 08:00

Preference distillation typically treats a teacher response as preferred and the student's own response as rejected. This assumes that self-generated failures are the most informative negatives and that rejects must come from a model at least as large as the student, making generation costly at scal

中文介绍 偏好蒸馏通常以教师回答为偏好样本、学生自身回答为拒绝样本，隐含假设自生成失败最有信息量且拒绝样本须来自不小于学生的模型，使规模化生成成本高昂。本文研究偏好蒸馏的规模规律，探索用更小模型产出更优质拒绝样本来降低开销。

Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation

👍 19

10/01 08:00

Extending a text embedding model to new modalities typically degrades text retrieval quality, and existing omni-modal embedders compensate with multi-billion parameters. We present Omni-Embed-Mini, a 0.9B-parameter model that maps text, speech, audio, images, video, and visually-rich documents into

中文介绍 将文本嵌入模型扩展到新模态通常会损害文本检索质量，现有全模态嵌入模型需数十亿参数来补偿。Omni-Embed-Mini 以 0.9B 参数将文本、语音、音频、图像、视频与富视觉文档映射到同一空间，通过密集蒸馏绑定多模态且不遗忘文本能力。

Designing MCP Gateway Uber's MCP Management Platform

@UberEng · 66.9K 粉丝 · 54.0K 阅 · 529 赞 · 59 转

10/03 01:21

Introduction Uber’s rapid adoption of AI agents has fundamentally changed how teams interact with code, data, and operational systems. Early ad-hoc integrations with the MCP (Model Context Protocol)

中文介绍 Uber 工程团队分享 MCP Gateway 的设计与落地。随着内部 AI agent 快速铺开，早期零散、临时的 MCP 集成方式已难以维护，文章讲述他们如何把代码、数据与运维系统的接入统一到一套 MCP 管理平台上，适合关注 agent 基础设施的人参考。

VIBE MANUFACTURING IS HERE

@gregisenberg · 724.2K 粉丝 · 44.1K 阅 · 569 赞 · 53 转

10/04 21:25

For the FIRST TIME in HISTORY, you can describe a physical product in one sentence and, a week later, hold it in your hands, cut from metal and built to your exact measurements. That's VIBE

中文介绍 博主提出「氛围制造」概念：第一次可以只用一句话描述一个物理产品，一周后就能拿到按你的尺寸切割、用金属做出来的实物。讨论 AI 把传统制造的设计到交付周期压缩到极致后，硬件创业门槛正在被重新定义。

Evals: how to know whether an AI system actually works

@sermakarevich · 549 粉丝 · 33.0K 阅 · 520 赞 · 64 转

10/04 02:38

An article for everyone who ships, buys, or signs off on software built on large language models (LLMs): engineers, product managers, and the CEO. Written in plain language, from the big picture down

中文介绍 一篇面向工程师、产品经理和 CEO 的 LLM 评测科普：用平实语言回答「怎么判断一个基于大模型的系统到底能不能用」。行文从整体图景往下拆解到具体方法，适合需要为 AI 产品拍板或验收的人补基础。

How to build a content machine with Opus 5.5 (Full Course)

@VibeMarketer_ · 37.8K 粉丝 · 31.4K 阅 · 504 赞 · 59 转

10/02 22:06

i'm going to show you how to build a content machine with Opus 5.5 that turns your ideas, research and expertise into articles, posts and newsletters in your own voice. from finding your next topic to

中文介绍 一套完整课程，教用 Opus 5.5 搭建内容生产机器：把想法、调研和专业积累转成保持你本人语气的长文、帖子和 newsletter，覆盖从找下一个选题到成稿发布的全流程，偏个人品牌内容工作流。

LIST OF HACKATHON FOR OCTOBER.

@onlyonealexia · 1.6K 粉丝 · 26.5K 阅 · 509 赞 · 53 转

10/04 10:08

A List of Web3, AI & Web2 Hackathons Compiled by @onlyonealexia There are a lot of hackathon lists that look impressive until you actually open the links. I compiled a list of ongoing and upcoming

中文介绍 整理了一份 10 月正在进行和即将开始的 Web3、AI 与 Web2 黑客松清单。博主吐槽很多同类列表看着唬人、点开链接却发现无效，这份是逐条核实过的，方便开发者挑赛事报名。

All games are open source now

@rehan_shei · 11.9K 粉丝 · 24.9K 阅 · 520 赞 · 49 转

10/04 08:52

A week ago I pointed Opus 5.5 at Terraria before bed. I woke up to a fully working, reverse engineered version of the game. Then I added a homing missile launcher, a tactical nuke and a bunch of new

中文介绍 博主睡前让 Opus 5.5 分析 Terraria，醒来就得到一个可运行的逆向工程版本，随后又自己加了追踪导弹、战术核弹等新武器。借此说明模型让游戏逆向与改 mod 的门槛大幅下降，并延伸谈「所有游戏都开源了」。

Reinforcement Learning from scratch #1

@goyal__pramod · 11.4K 粉丝 · 24.7K 阅 · 504 赞 · 59 转

10/03 12:45

Now I am aware you must have been looking forward to the second part of my CUDA blog (if you haven't read it yet, check it out!), but hey! A man can have varied interests. And I believe if you are

中文介绍 博主在 CUDA 博客系列之间插开新坑「从零实现强化学习」第一篇，延续其手写实现、不依赖现成框架的风格，带领读者从基础概念和代码入手理解 RL，适合想补底层直觉的人跟进。

The Agent Said It Was Done. The Database Disagreed.

Hugging Face Blog · 10/04 06:56

中文介绍 微软在 Hugging Face 博客发文，讨论 AI 智能体自报任务已完成、但数据库实际状态与之不符的问题，聚焦智能体执行结果与系统真实状态之间的偏差。

[AINews] not much happened today

Latent Space · 10/03 16:45

a quiet day.

中文介绍 Latent Space 的 AI 新闻栏目「AINews」本期显示，当日人工智能领域消息相对平静，未出现值得关注的重大动态。

A model guide for the GPT-6 family

OpenAI News · 10/03 00:15

Learn how startups can choose GPT-6 models, tune reasoning effort, improve prompts and skills, coordinate tools, and prepare workflows for production.

中文介绍 OpenAI 发布 GPT-6 系列模型使用指南，介绍初创企业如何选择模型、调节推理强度、优化提示词与技能、协调工具调用，并为生产环境准备相应工作流。

Redefining enterprise intelligence with autonomous AI

MIT Tech Review AI · 10/02 23:49

Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year. F

中文介绍 文章指出，企业级 AI 已进入实际运营阶段，模型能力提升速度超过多数机构的吸收能力，性能成本持续下降；2026 年全球 AI 投资预计达 2.5 万亿美元，同比增长 44%。

Open-sourcing AstaBrief, the fast report-generation model in Asta

Hugging Face Blog · 10/02 23:19

中文介绍 艾伦人工智能研究院（AI2）在 Hugging Face 开源 AstaBrief，这是其 Asta 平台中用于快速生成报告的模型。

Inside-Out AI: Rebuilding Airbnb Behind the Scenes and Across the Guest Experience

Latent Space · 10/02 22:04

After leading Meta’s Llama models, Ahmad Al-Dahle is now transforming Airbnb with AI — from how its teams develop products to how it serves guests.

中文介绍 曾主导 Meta Llama 系列模型的 Ahmad Al-Dahle 目前正在 Airbnb 推动 AI 转型，涉及团队内部的产品开发方式以及面向房客的服务体验。

Don’t be fooled—LLMs don’t reason

MIT Tech Review AI · 10/02 16:00

On an afternoon in Seoul in March 2016, I watched a program I helped build put a stone on the fifth line of a Go board in what looked like a gift to its human opponent. Move 37 in game two of the five-game match looked so absurd that some commentators thought it was a…

中文介绍 文章认为大语言模型并不具备真正的推理能力。作者以 2016 年 3 月首尔人机围棋对决第二局第 37 手为例，回顾当时被评论者视为匪夷所思的一步棋。

[AINews] Pi 1.0, Pi Durable, and AIE NYC

Latent Space · 10/02 14:40

the minimalist harness goes stable... and TypeScript!

中文介绍 Latent Space 的「AINews」介绍 Pi 1.0 与 Pi Durable：这一极简智能体框架进入稳定版本，并支持 TypeScript，同时提及 AIE 纽约活动。

AutoSynthData: Generating Training Data for Enterprise Agents

Hugging Face Blog · 10/02 12:01

中文介绍 ServiceNow 在 Hugging Face 博客介绍 AutoSynthData，一种面向企业级 AI 智能体的训练数据自动生成方案。

Academia is for Ambition — Alex Zhang, MIT

Latent Space · 10/02 08:28

We catch up with RLM first author Alex Zhang, MIT PhD, on Jev, PhD masxing, and the future of harnesses.

中文介绍 Latent Space 对话 RLM 论文第一作者、麻省理工学院博士生 Alex Zhang，讨论其研究动机、读博经历，以及智能体框架（harness）的未来发展。

Chatham scales its capital markets expertise with OpenAI

OpenAI News · 10/02 08:00

Chatham Financial uses Codex and GPT-5.6 to build technology and redesign workflows, cutting trade validation from 30 minutes to under 4.

中文介绍 Chatham Financial 使用 Codex 与 GPT-5.6 开发技术并重塑工作流程，将交易验证所需时间从 30 分钟缩短至 4 分钟以内。

Decision models 🤖, Claude-shaped science 🧪, OpenAI safety firings 🚨

TLDR AI · 10/02 08:00

中文介绍 TLDR AI 当日通讯涵盖三个话题：决策模型、与 Claude 相关的科学研究进展，以及 OpenAI 安全团队人员被解雇的消息。

The eternal complement

OpenAI News · 10/02 01:00

Advanced AI may matter most for the routine work behind breakthrough ideas. Explore why execution could shape the next economy and the pace of progress.

中文介绍 OpenAI 文章认为，先进 AI 的价值可能更多体现在突破性构想背后的日常执行工作上，执行效率或将影响下一阶段经济形态与技术进步速度。

How Albertsons Companies is reimagining retail from the inside out

OpenAI News · 10/02 00:00

Albertsons Cos. is using ChatGPT Enterprise and the OpenAI API to help teams work faster and make grocery shopping easier for millions of customers.

中文介绍 美国零售企业 Albertsons 正利用 ChatGPT 企业版与 OpenAI API 提升内部团队工作效率，并为数百万顾客简化购物流程。

[AINews] Gemini 4 Argon: GDM’s answer to Astra/Fable, with 1M output

Latent Space · 10/01 14:45

... but you can’t try it yet unless you are “government users and trusted cyber defenders in the Fairwind Program”

中文介绍 Latent Space 的「AINews」报道谷歌 DeepMind 发布 Gemini 4 Argon，支持 100 万规模输出，被视为对 Astra/Fable 的回应；目前仅向「Fairwind 计划」中的政府用户与受信任网络防御人员开放。