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Prime Intellect engineers explained how they train reasoning models over the open internet in 30 minutes - better than $3000 distributed training courses. split policy and rollouts across nodes -> run agents in parallel envs -> verify with LLM judges -> gradient-sync over the internet -> train Llama, Qwen,... show more

h100envy

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39,348 görüntüleme • 11 gün önce •via X (Twitter)

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Alibaba engineer who leads Qwen explained the future of open agent models in 25 minutes - better than $2000 LLM training courses. pre-train the base ->SFT -> RLHF -> tool use -> multi-modal -> ship a whole family (chat / VL / coder / math / QwQ). That loop is why Qwen quietly became the most downloaded open model family on Hugging Face. Qwen base + Qwen-VL + Qwen-Coder + QwQ reasoning - that's the stack. Watch and save it, then read the article below.

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The network for machine intelligence Two years ago, we laid out our vision for a machine learning compute protocol. One that connects every device in the world into an open network for machine intelligence, with no gatekeepers or artificial boundaries. This week, we’ll be sharing some of our early progress, beginning with RL Swarm, a peer-to-peer system for collaborative reinforcement learning over the internet. Next month, we’ll open our Testnet, allowing anyone to contribute to the frontier of open machine intelligence. Introducing RL Swarm RL Swarm is a fully open source system for collaborative reinforcement learning over the internet. It is a live demo of our research findings, which show that models training with RL learn faster when they train as a collective swarm than they do on their own. Join our swarm now to see this in practice. You can participate with consumer hardware at home or a powerful GPU in the cloud. You can follow along with the swarm’s progress by following the links below.

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Andrew Ng

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Jiaxuan You

58,205 görüntüleme • 7 ay önce

PyTorch core engineer at Meta turned CUDA kernel writing into a sport in 13 minutes - better than $1500 GPU programming bootcamps. profile the kernel -> find the bottleneck -> rewrite -> benchmark -> merge the winning code into PyTorch. That loop is how the open community now beats hand-tuned vendor kernels. GPU MODE community + KernelBot competition + winning kernel merged into the framework - that's the stack. Watch it, then steal the loop below.

h100envy

35,390 görüntüleme • 22 gün önce

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Thanksgiving-week treat: an epic conversation on Frontier AI with Lukasz Kaiser -co-author of “Attention Is All You Need” (Transformers) and leading research scientist at OpenAI working on GPT-5.1-era reasoning models. 00:00 – Cold open and intro 01:29 – “AI slowdown” vs a wild week of new frontier models 08:03 – Low-hanging fruit, infra, RL training and better data 11:39 – What is a reasoning model, in plain language 17:02 – Chain-of-thought and training the thinking process with RL 21:39 – Łukasz’s path: from logic and France to Google and Kurzweil 24:20 – Inside the Transformer story and what “attention” really means 28:42 – From Google Brain to OpenAI: culture, scale and GPUs 32:49 – What’s next for pre-training, GPUs and distillation 37:29 – Can we still understand these models? Circuits, sparsity and black boxes 39:42 – GPT-4 → GPT-5 → GPT-5.1: what actually changed 42:40 – Post-training, safety and teaching GPT-5.1 different tones 46:16 – How long should GPT-5.1 think? Reasoning tokens and jagged abilities 47:43 – The five-year-old’s dot puzzle that still breaks frontier models 52:22 – Generalization, child-like learning and whether reasoning is enough 53:48 – Beyond Transformers: ARC, LeCun’s ideas and multimodal bottlenecks 56:10 – GPT-5.1 Codex Max, long-running agents and compaction 1:00:06 – Will foundation models eat most apps? The translation analogy and trust 1:02:34 – What still needs to be solved, and where AI might go next

Matt Turck

168,003 görüntüleme • 7 ay önce

Amjad Masad and Marc Andreessen on AI agents, AGI, Creativity, and Reasoning Agents are getting better, fast. Replit Agent v1 could do only 2 minutes of coherent work. v2 could do 20 minutes. Today’s v3? 200 minutes. As agents get more and more reliable, we can make huge progress in coding without ever achieving “true” AGI. In this episode, Marc Andreessen and Erik Torenberg sit down with Replit ⠕ co-founder and CEO Amjad Masad to discuss the upward trajectory of agents, how RL unlocked reasoning for modern models, why verification loops changed everything, and whether LLMs are hitting diminishing returns. 00:00 Intro 03:00 The vision behind Replit 07:00 Building apps with AI agents 09:30 When the agent becomes the programmer 13:45 Reinforcement learning and problem solving 17:30 The verification loop and multi-agent systems 26:00 Why coding is advancing faster than other Fields 33:45 AGI debate 41:15 Functional AGI and automating labor 53:10 Creativity, reasoning, and finding truth in AI 57:30 The origins of Replit 01:03:00 Hacking his university and getting caught Amjad Masad Marc Andreessen 🇺🇸 Erik Torenberg

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a16z

740,966 görüntüleme • 9 ay önce