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: Lean 4 Theorem Proving

Multi-turn formal theorem proving in Lean 4, where models alternate between reasoning, sketching proof code, receiving feedback.

Ideal for search guided rl, process rewards, and curriculum design.

By @myainotez

app.primeintellect.ai/dashboard/envi…

Join the Prime Intellect RL Residency

Our community already shipped 100+ environments to the Environment Hub

Help us accelerate, with compute, a stipend, and support from our research team The RL Residency gives you:
— Compute for experiments
— A stipend
— Hands-on support from our internal research team

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 Environments are where agents learn.

They define the world, rules, and feedback loop of state → action → reward. Everything from coding/math tasks to games and multi-turn dialogue evals can be thought of as environments. Without them, RL is just math with nothing to interact with.

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. Planetary-Scale Inference
Our peer-to-peer decentralized inference stack moves into production, enabling everyone—from consumer GPUs to hyperscale clusters—to contribute meaningfully towards open-source AI progress.

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

primeintellect.ai/blog/intellect… To train a model with reinforcement learning in a fully decentralized setting using community-contributed GPUs, we open-source several novel infrastructure components.

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. INTELLECT-2 brings decentralized training into the inference-time compute era:
• Fully async, decentralized reinforcement learning
• Eliminating communication overhead
• Scalable across heterogeneous GPUs worldwide

primeintellect.ai/blog/intellect…

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. Today, we release:
- SYNTHETIC-1: 1.4 million high-quality tasks & verifiers
- Public synthetic data run - allowing anyone to contribute compute
- GENESYS: open, extendable synthetic data generation framework + call for crowdsourcing tasks & verifiers

primeintellect.ai/blog/synthetic…

Today, we release TOPLOC: A Locality Sensitive Hashing Scheme for Verifiable Inference

- Detects modifications to models, prompts, or precision
- Robust across GPU types, tensor parallel configurations and attention kernels
- Up to 100× faster validation than generation
- Reduces memory overhead of proofs by 1000×

primeintellect.ai/blog/toploc

Building the foundation for decentralized, verifiable compute protocols. The Problem: Trust in LLM Inference

In a peer-to-peer setting, ensuring honest behavior among providers requires detecting and penalizing dishonest ones. Providers often make changes, such as:

- Lowering precision
- Compressing KVCache
- Altering model weights or prompts

Releasing METAGENE-1: In collaboration with researchers from USC, we're open-sourcing a state-of-the-art 7B parameter Metagenomic Foundation Model.

Enabling planetary-scale pathogen detection and reducing the risk of pandemics in the age of exponential biology. METAGENE-1 is a 7B parameter autoregressive transformer model trained on over 1.5T DNA and RNA base pairs sequenced from wastewater samples.

Website: metagene.ai
Paper: metagene.ai/metagene-1-pap…
Github: github.com/metagene-ai/me…
Hugging Face: huggingface.co/metagene-ai

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 This represents a 10× scale-up from our previous work and demonstrates that large-scale model training is no longer confined to large corporations but can be achieved through distributed, community-driven approaches.

Technical report: github.com/PrimeIntellect…

Blogpost: primeintellect.ai/blog/intellect…

Introducing Prime Intellect – democratizing AI development at scale, from compute to intelligence.

We're excited to announce our $5.5M raise from @DistributedG @coinfund_io @CompoundVC @Collab_Currency @protocollabs @ClementDelangue @dylan522p and others

primeintellect.ai/blog/introduci… Our vision
Build infrastructure to aggregate compute, develop distributed training frameworks, and create a protocol for decentralized AI development—enabling anyone to contribute resources, collectively train open models, and share in their ownership.