Weikai Huang @BrightChihiro

这条推文的含金量还在上升 Follow Builders, Not Influencers~ @karpathy — 前 OpenAI/Tesla AI，现 Eureka Labs，AI 教育传奇 @swyx — AI Engineer 运动发起人，Latent Space 播客主理人 @joshwoodward — Google Labs VP，负责 Gemini App 和 AI Studio @kevinweil — 前 OpenAI CPO，前 Instagram/Twitter 产品负责人 @petergyang — Roblox 产品领导，Behind the Craft 作者 @thenanyu — Linear Head of Product，一线 AI 产品构建者 @realmadhuguru — Google Gemini 产品领导，推动"快速构建"文化 @AmandaAskell — Anthropic 哲学家，塑造 Claude 的人格与品格 @_catwu — Anthropic Claude Code 产品负责人 @trq212 — Anthropic Claude Code 工程师，深度分享 AI Agent 实践 @GoogleLabs — Google 官方 AI 实验账号 @amasad — Replit CEO，AI 编程工具推动者 @rauchg — Vercel CEO，Next.js 作者 @alexalbert__ — Anthropic Claude 团队 PM @levie — Box CEO，企业级 AI 与商业趋势洞察 @ryolu_ — Cursor Head of Design，前 Notion/Stripe @garrytan — Y Combinator CEO，AI 创业生态 @mattturck — AI 投资人，MAD Podcast 主理人 @zarazhangrui — follow-builders 项目作者，AI Builder & 策展人 @nikunj — FPV Ventures 合伙人，AI 时代 SaaS 思考 @steipete — iOS/macOS 开发传奇，现聚焦 AI 开发工具 @danshipper — Every 创始人，探讨 AI 对工作与创造的影响 @adityaag — South Park Commons GP，前 Dropbox CTO @sama — OpenAI CEO— Anthropic 官方 Claude 账号 @mumaren_2 —木马人，多年大厂经验，专注AI领域知识和工具分享

Allen @mumaren_2

a month ago

是不是烦透了时间线上的垃圾内容？ 然后刷了半天刷不到有用信息？ 今天分享一个X的小技巧 只要3步帮你轻松解决信息源的问题！ 1.选择你喜欢的博主，点击右上角，从列表中添加 2.选择添加列表，自定义列表名称，比如我这里有个AI信息源，就是所以我觉得好的AI博主的列表，看到就更新

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@sirbayes Super clever idea to make smaller LLMs reliable in TextArena! Quick question: do you plan to open-source the code? Would love to try it out and build on it. Thanks!

@xjhylmy @Jiaxi_Cui

Great question! Two main lenses: 1. Systems: most prior frameworks share the similar conceptual loop but are tightly coupled, so changing one component often requires rewriting the system. 2. Algorithmic: many existing methods rely on fixed strategies/parameters even though the search dynamics change over time. That motivated a modular framework + adaptive algorithms that can adjust (AdaEvolve), or even evolve the evoler (EvoX) itself!

@victor_wu autoskill好像是这个思想

@shulynnliu Exciting works! What lens did you use to identify the shortcomings of the previous work?

@emrecanacikgoz Do you think self-play will be widely adopted by industry and academia to improve model capabilities? 👀

Tired of debugging LLMs by reading the extremely long chain of thoughts? We built Landscape of Thoughts (LoT) to transform complex thoughts into intuitive visual maps to help you understand model behaviors. Paper and findings in 🧵 1/10 youtu.be/Zb8CfYxSvik?si… via @YouTube

There are traditionally two types of research: problem-driven research and method-driven research. As we’ve seen with large language models and now AlphaEvolve, it should be very clear now that total method-driven research is a huge opportunity. Problem-driven research is nice because you have a consistent and specific goal. The goal is usually virtuous, so it feels good to have a mission and identity. However, it just doesn’t work due to The Bitter Lesson. Basically everything in classical NLP (machine translation, summarization, chatbots) lost to simple scaling. ChatGPT is a prime example—it used nothing from chatbot research and certainly wasn’t the intended end goal of OpenAI’s 2022 research program, but was a huge hit because someone (John Schulman et al) figured out the right way to package large language models as a product. Method-driven research feels less stable because you’re constantly searching for problems and you have to be opportunistic. But I believe AI will allow method-driven research to dominate progress in most fields of science, one-by-one. The latest method (or “hammer”), as we’ve seen in AlphaEvolve, is ruthless search and optimization against a reward function (whether this requires RL or not is a separate discussion). Things that problem-driven researchers have been trying to solve for a long time like the kissing number problem will become nails hit by the hammer. Eventually the hammer will become bigger, stronger, and more general and will hit more and more nails. So a very important meta-skill for the next decade will be knowing how to create the right environments to use The Hammer. Ironically, the problem-driven researchers, who by definition are experts in a specific problem, are well-positioned to create these environments. If, that is, they can put down their egos and pick up the hammer.

Introducing VerlTool - a unified and easy-to-extend tool agent training framework based on verl. Recently, there's been a growing trend toward training tool agents with reinforcement learning algorithms like GRPO and PPO. Representative works include SearchR1, ToRL, ReTool, and ToolRL. While these achieve impressive performance, their training codes are either not fully open-sourced or too difficult to modify and customize with new tools, creating unexpectedly high engineering costs for the community when exploring new ideas. To address these issues and reduce engineering overhead, we propose verl-tool. Key Features: 1. 🔧 Complete decoupling of actor rollout and environment interaction - We use verl as a submodule to benefit from ongoing verl repo updates. All tool calling is integrated via a unified API, allowing you to easily add new tools by simply adding a Python file and testing independently. 2. 🌍 Tool-as-environment paradigm - Each tool interaction can modify the environment state. We store and reload environment states for each trajectory. For each training, you can launch 3. ⚡ Native RL framework for tool-calling agents - verl-tool natively supports multi-turn interactive loops between agents and their tool environments. 4. 📊 User-friendly evaluation suite - Launch your trained model with OpenAI API alongside the tool server. Simply send questions and get final outputs with all interactions handled internally. We've successfully reproduced ToRL results using our verl-tool framework, demonstrating its correctness and demonstrating comparable performance on mathematical benchmarks. VerlTool is an active ongoing project! We aim to incorporate more tools covering a wide range of use cases and expect they can be trained together in a single framework. Suggestions and contributions are highly welcomed! Check out our GitHub: github.com/TIGER-AI-Lab/v… More details: 👇 (0/4)