The next wave of AI will not be won by better prompts. It will be won by systems that learn from experience.
Today, Prime Intellect Lab is out of beta, open for you to start training your own models.
The era of self-improving agents is here.
Today, we’re sharing how our collaboration with @nvidia helps power the open superintelligence stack.
The next frontier of AI infrastructure is building systems for agentic models that can reason for hours, use tools, execute code, and learn from outcomes at scale.
https://t.co/PyHnfxeXL4
Introducing Lab: A full-stack platform for training your own agentic models
Build, evaluate and train on your own environments at scale without managing the underlying infrastructure.
Giving everyone their own frontier AI lab.
Introducing INTELLECT-3: Scaling RL to a 100B+ MoE model on our end-to-end stack
Achieving state-of-the-art performance for its size across math, code and reasoning
Built using the same tools we put in your hands, from environments & evals, RL frameworks, sandboxes & more
Environments Hub launched a week ago, and we’ve already crowdsourced 100+ environments.
Ranging from theorem proving, kernel generation, scientific qa, browser-use, and more. Every environment contributed shifts the balance of power towards open-source AI.
Some highlights:
Introducing the Environments Hub
RL environments are the key bottleneck to the next wave of AI progress, but big labs are locking them down
We built a community platform for crowdsourcing open environments, so anyone can contribute to open-source AGI
Launching SYNTHETIC-2: our next-gen open reasoning dataset and planetary-scale synthetic data generation run.
Powered by our P2P inference stack and DeepSeek-R1-0528, it verifies traces for the hardest RL tasks.
Contribute towards AGI via open, permissionless compute.
Releasing INTELLECT-2: We’re open-sourcing the first 32B parameter model trained via globally distributed reinforcement learning:
• Detailed Technical Report
• INTELLECT-2 model checkpoint
https://t.co/tIKbtUlJQH
We did it — the first decentralized RL training of a 32B model is complete!
Full open-source release is coming in ~1 week, including: checkpoints, data and a detailed technical report.
Today we’re launching INTELLECT-2:
The first decentralized 32B-parameter RL training run open to join for anyone with compute — fully permissionless.
Scaling towards frontier reasoning across coding, math and science.
Introducing SYNTHETIC-1: Collaboratively generating the largest synthetic dataset of verified reasoning traces for math, coding and science using DeepSeek-R1.
Join us to contribute compute towards state-of-the-art open reasoning models.
Releasing INTELLECT-1: We’re open-sourcing the first decentralized trained 10B model:
- INTELLECT-1 base model & intermediate checkpoints
- Pre-training dataset
- Post-trained instruct models by @arcee_ai
- PRIME training framework
- Technical paper with all details
Releasing INTELLECT-1: We’re open-sourcing the first decentralized trained 10B model:
- INTELLECT-1 base model & intermediate checkpoints
- Pre-training dataset
- Post-trained instruct models by @arcee_ai
- PRIME training framework
- Technical paper with all details
Announcing INTELLECT-1: the first-ever decentralized training of a 10B model
Scaling decentralized training 10x beyond prior efforts.
Anyone can join us to build open-source AGI 🦋
Introducing OpenDiLoCo, an open-source implementation and scaling of DeepMind’s Distributed Low-Communication (DiLoCo) method, enabling globally distributed AI model training.
https://t.co/LrKGDoGXJK