Introducing Prime Inference: We've served trillions of tokens for RL and dedicated customer deployments To own your intelligence, you need to own your inference Unpacking our inference stack

The next frontier is AI that improves itself, and we believe everyone should know how to build it. This semester, Ao @ao_qu18465 and I are teaching 'How to AI an AI for Almost Anything' @MIT @medialab -- a new course on self-evolving AI systems that recursively improve from experience, feedback, memory, and exploration over long interaction horizons. The key idea: intelligence shouldn't stop improving when training ends. We cover four levels of evolution: (1) context evolution through prompt optimization and context management, (2) harness evolution through memory, tools & skills, workflow optimization, and multi-agent systems, (3) model evolution through RL, distillation, test-time training, self-play, and continual learning, (4) task evolution through generating increasingly complex synthetic tasks, simulations, evaluations, and environments. Slides, readings, and videos available: mit-mi.github.io/how2ai-cour… We thank @Kimi_Moonshot and @PrimeIntellect for generously sponsoring API credits!

We’re releasing more open models to help teams across Europe build AI with their own data, on infrastructure they control. Announced at GTC Berlin, the models help developers, researchers and enterprises build specialized AI agents, train robots, design proteins and develop quantum computers. See what’s new: nvda.ws/4iwBMvF

applied research at prime intellect is both a client-facing team and a fundamental research team. continual learning is our north star. we view this dual approach as necessary in order to study the core challenges faced by models which self-improve in production. the real world is big and messy. we spend part of our our time working with customers to train SOTA models for specialized domains, and we automate and productize our work as we go. we also do open-ended research on novel methods for self-improvement and long-horizon memory, and we get to experiment with data that no other lab gets to see. today, the most readily-deployable form of continual learning is converting "exhaust" (agent traces, logs, feedback) into robust environments and tasks for continual RL. but this is not the final form. there are major open challenges around knowledge acquisition, harness-weight tradeoffs, user modeling, dynamic underspecified preferences, updating and planning across timescales, selective forgetting, data efficiency, learning from golden answers, and company-scale simulation. this is the roadmap, roughly speaking. some of these may require changes to architecture, or parameter-efficient methods, or fundamentally new algorithms. we have our hypotheses and early evidence, but everything is on the table. the world is our oyster. we are betting on a radically different future of what ai deployment will look like, and we can see the path more clearly every day. it is a world where agents learn like humans do, with billions of individual models compounding their learnings from the real world continuously. we're hiring incredibly ambitious researchers in SF, NY, and London. come join us.

over the past few months we've been rapidly scaling our partnerships with leading companies across a bunch of incredibly exciting domains we collaborate with their in-house experts to deploy continual learning loops in prod hiring for many new roles :) primeintellect.ai/careers

Excited to announce that I've joined @PrimeIntellect! Our mission is to build the open training stack and let people own their intelligence. Prime Intellect has always gone a step beyond open-weight models and strived for a fully open-source ecosystem by sharing everything you can think of: training infra, data, evaluations, inference, algorithms, harnesses, and more. In just a few weeks, I’ve already learned so much from an incredibly goated team with deep expertise across the entire model stack. Looking forward to building frontier open models with the team. Watch out for what we are cooking, big things ahead!

Recursive self-improvement is being hyped to the moon and back. I wanted to know how good frontier models actually are at a less-studied piece of it: curating pretraining data. So I built PretrainGym at the @PrimeIntellect RL Residency 🧵 justin-chae.org/blog/pretrai…

Read our blog post here: primeintellect.ai/blog/prime… prime-rl 0.10.0: github.com/PrimeIntellect-ai… verifiers 0.4.0: github.com/PrimeIntellect-ai… Thank you to everyone who contributed, including 11 new prime-rl contributors

All of this runs on one eval stack. Run evals in prime-rl: any model, any environment, either standalone or as online evals during a run verifiers 0.4.0 ships alongside. Everything is v1 (v0 removed) and the command-line tools are all vf-* now

Every architecture in prime-rl now runs on our own optimized implementation New in this release: DeepSeek V4 Flash, Qwen3.8 Flash Next, Nemotron-H Nano and Super, and multimodal RL with Qwen-VL and Kimi K2.5

We tested it in a deliberately hard setting: agentic SWE RL with GLM-4.5-Air • IcePop collapsed around step 1.2k when trainer-inference KL blew up 20× • IPO trained straight through, masking only ~0.25% of tokens

INTELLECT Policy Optimization (IPO) In async RL, trainer and inference never agree exactly on a token's probability. IcePop masks on the ratio, which misjudges both tails IPO masks on the probability difference instead

With GLM-5.3, sparse attention now runs the forward pass with FlashMLA and the backward with cuDNN, plus a 12-node 16k recipe with no context parallelism or offloading On full SFT, throughput is 1.8× our previous recipe

Faster training Qwen3-235B-A22B SFT on the same nodes as torchtitan (4×8 B300, 16k seq) is 1.15× faster with the same memory Most of the gain is MoE work: fused gate/up and QKV projections, fullgraph compile, chunked loss, and SonicMoE BF16 backend

Today we're releasing prime-rl v0.10.0 and verifiers v0.4.0 We added our new RL algorithm IPO as the default, tailored for async RL stability, as well as performance improvements and new model support

prime intellect open superintelligence research day friday 23rd october berlin join us luma.com/primeintellect-berl…

Wanted to explore how well a language model can run a robot with just code, cameras and motors Had spent some time building an environment @PrimeIntellect residency to test and train these models on robotic manipulation using a set of tasks from existing benchmarks/suites

Come meet the Prime Intellect team in Berlin this Friday. We're hosting a very special event showcasing never-before-seen pieces by a Venice Biennale artist. Link below

DeepSeek-v4.1-Flash-1017 is now available on Prime Inference