Xiao @LambdaShi
归去,也无风雨也无晴 . Think Systems in AI. Think AI in Systems. [email protected] github.com/lambda7xx Joined February 2022-
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NVIDIA says SLMs are the future of agents and it makes sense: agents mostly do a small set of repetitive tasks that small language models are already good at. using heavy LLMs for these tasks is like buying a supercomputer for high-school math. you can have 10-30x cost…
We’re live! 🎉 This is the official account for slime — an open-source, SGLang-native post-training framework for RL scaling. Kicking things off with our first milestone → v0.1.0 release 🧪 Blog: thudm.github.io/slime/blogs/re… Follow us to run RL faster ⚡️
This is huge Continuing our foundational work to enable anyone to train state of the art AI model, we’re thrilled to release « FinePDFs » 3T tokens of textual data that until now was locked away in PDFs, arguably some of the highest quality publicly available data out there.…
This is huge Continuing our foundational work to enable anyone to train state of the art AI model, we’re thrilled to release « FinePDFs » 3T tokens of textual data that until now was locked away in PDFs, arguably some of the highest quality publicly available data out there.…
The most important AI paper of 2025 might have just dropped. NVIDIA lays out a framework for Small Language Model agents that could outcompete LLMs. Here’s the full breakdown (and why it matters):
How well can LLMs & deep research systems synthesize long-form answers to *thousands of research queries across diverse domains*? Excited to announce 🎓📖 ResearchQA: a large-scale benchmark to evaluate long-form scholarly question answering at scale across 75 fields, using…
🚀 Excited to share my new blog post (co-authored with @zhuzilinallen @Ji_Li_233) on efficient RL weight synchronization—a deep dive into design & implementation details! 📖 Read it here: hebiao064.github.io/rl-weight-sync 🇨🇳 中文版在知乎也有 👉 zhuanlan.zhihu.com/p/194567184868… #RL #slime #SGLang
设计非常牛批的 Knob 键盘终于上市了,售价 439 美元,实体质感也很好
🔥We are pleased to announce a new code walkthrough for slime, the open-source RL framework that has become a cornerstone in many RL pipelines. This walkthrough is designed to illuminate the inner workings of the framework, moving beyond surface-level usage to explain how…
With @EPFL_en , @ETH_en, and @cscsch today released *Apertus*, Switzerland's first large-scale, multilingual language model (LLM). As a fully open LLM, it serves as a building block for developers and organizations to create their own applications.🧵 ethz.ch/en/news-and-ev…
New research agenda we're kickstarting at Berkeley: redesigning data systems to serve the dominant workload of the future: agents! Agentic speculation is massive, heterogeneous, steerable, and redundant: properties data systems can better support and take advantage of. Take a…
Congrats to @Peiqi_Yin on the release of Gorgeous A gorgeous piece of work that takes a thoughtful look at existing disk-based ANN systems, significantly improves I/O efficiency, and achieves huge end-to-end gains by relayouting the data structure Shares a lot of similarity with…
Congrats to @Peiqi_Yin on the release of Gorgeous A gorgeous piece of work that takes a thoughtful look at existing disk-based ANN systems, significantly improves I/O efficiency, and achieves huge end-to-end gains by relayouting the data structure Shares a lot of similarity with…
An amazing collaboration with the Biomni team! We introduce Biomni-R0, a reasoning-enabled multi-task multi-tool biomedical research agent, trained through end-to-end RL. The results are impressive -- 2x stronger than the base model and >10% better than GPT 5 and Claude 4 Sonnet!…
An amazing collaboration with the Biomni team! We introduce Biomni-R0, a reasoning-enabled multi-task multi-tool biomedical research agent, trained through end-to-end RL. The results are impressive -- 2x stronger than the base model and >10% better than GPT 5 and Claude 4 Sonnet!…
"Universities must be independent sources of data, theory, and argument. Faculty members and students should have the incentive, the responsibility, and the freedom to pursue scholarly excellence even when the arguments they generate might anger or displease powerful people. That…
我们团队最近一个月的成果终于开源了,MedResearcher-R1 是一个医学领域的 DeepResearch Agent 模型,我们不仅开源了模型,还开源了合成数据的 pipeline,搭配开箱即用的前端页面。而且这个框架是通用的,并不局限于医学领域。用这个框架训练出来的模型在开源的榜单上面取得了不错的成绩。…
Live benchmark for deep research systems, this is huge! 🔥
Great interview with my awesome colleagues @19kaushiks @robertriachi @m__dehghani and @nbrichtova by @OfficialLoganK about the new image generation and editing capabilities in our latest Gemini Flash 2.5 model (also known as nano banana 🍌)
Great interview with my awesome colleagues @19kaushiks @robertriachi @m__dehghani and @nbrichtova by @OfficialLoganK about the new image generation and editing capabilities in our latest Gemini Flash 2.5 model (also known as nano banana 🍌)
just a friendly reminder that our first session of the @GPU_MODE x @scaleml series starts at 1:15pm ET / 10:15am PT today on the GPU MODE YouTube channel! the first day will feature a talk by the amazing @sewon__min on FlexOlmo, so you don't want to miss it :)
“TogetherAI’s chief scientist @tri_dao announced Flash Attention v4 … uses CUTLASS CuTe Python DSL” As always, thanks for being the tip of the spear and pushing us along too 💚
“TogetherAI’s chief scientist @tri_dao announced Flash Attention v4 … uses CUTLASS CuTe Python DSL” As always, thanks for being the tip of the spear and pushing us along too 💚
🔥 DeepCode has been trending on GitHub for 2 consecutive days! 🚀 Almost hitting 2k GitHub Stars! 🌟 Fully Open Source: github.com/HKUDS/DeepCode ✨ All-in-One Agentic Coding Framework ✨ • 📄 Paper2Code - Research to Implementation • 🌐 Text2Web - Natural Language to Frontend…
We released DeepConf that can achieve 99.9% on AIME'25 with open source models with only 15% of the compute, compared to majority voting@512. The secret? Simple. Just to pruning the rollouts if they show a consecutive stream of low-confidence😀. Can be applied to any models…
We released DeepConf that can achieve 99.9% on AIME'25 with open source models with only 15% of the compute, compared to majority voting@512. The secret? Simple. Just to pruning the rollouts if they show a consecutive stream of low-confidence😀. Can be applied to any models…

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