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Read our blog post here: primeintellect.ai/blog/prime-rl-… prime-rl 0.10.0: github.com/PrimeIntellect… verifiers 0.4.0: github.com/PrimeIntellect… 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…
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
we just spun up a full k8s cluster inside our sandboxes thanks to the new LAN feature: private networking between a cluster of sandboxes. leaving it running over the weekend to prototype an idea back Monday to check the hillclimb results. agent swarms will replace traditional software development. enterprises will order hyper-optimized systems end-to-end, unattended.
Can agents discover the physical laws of artificial worlds? During my residency at @PrimeIntellect I built PhySim to find out. swpo.github.io/physim/
Moondream 3 Preview and Moondream 3.1 from @moondreamai are now on OpenRouter, served by @PrimeIntellect Object detection and pointing work in plain chat: attach one image and start your message with /detect {"object":"coffee cup"} or /point {"object":"croissant"}
Moondream 3 Preview and Moondream 3.1 are live on OpenRouter Small vision-language models (9B total, 2B active MoE): image + text in, 32K context, optional reasoning. $0.30/M input tokens $2.50/M output tokens x.com/OpenRouter/sta…
Moondream 3 Preview and Moondream 3.1 from @moondreamai are now on OpenRouter, served by @PrimeIntellect Object detection and pointing work in plain chat: attach one image and start your message with /detect {"object":"coffee cup"} or /point {"object":"croissant"}
Moondream 3 is (finally) on OpenRouter!
Moondream 3 Preview and Moondream 3.1 from @moondreamai are now on OpenRouter, served by @PrimeIntellect Object detection and pointing work in plain chat: attach one image and start your message with /detect {"object":"coffee cup"} or /point {"object":"croissant"}
Had a great time collaborating with @NVIDIAAI on Nemotron 3.5 Super VL! AutomationBench is a great testbed for evaluating agents on complex, real-world enterprise workflows. So much more to build here, excited to keep pushing! 🚀
Together with NVIDIA we post-trained Nemotron 3.5 Super VL on thousands of tasks Reflecting enterprise workflows in sales, operations, marketing, finance We improved the AutomationBench-AA score by ~70% on the public split
Full writeup here: sword.com/research/intro…
Sword Health built Dawn 1 on prime-rl: They RL-post-trained GLM-5.3 into a mental health support model that is safer, less sycophantic, and more cost-efficient than its base
Introducing Dawn 1, Sword's frontier model for mental health support. Post-trained from GLM-5.3, Dawn 1 delivers greater intelligence at lower inference cost, with strong performance across mental health knowledge, safety, and sycophancy evaluations. It also outperforms frontier
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