We're scaling our Open-Source Environments Program
As part of this, we're committing hundreds of thousands of $ in bounties and looking for partners who want to join our mission to accelerate open superintelligence
Join us in building the global hub for environments and evals
Over the past 2 months, we've crowdsourced 400+ environments and 80+ verified implementations through our bounties and RL residency across:
+ Autonomous AI Research
+ Browser Automation
+ Theorem Proving
+ Subject-Specific QA
+ Legal/Finance Tasks
+ Many more...
Thank you to everyone whose claimed a bounty or joined the residency!
Today, we're releasing a new wave of bounties, with some worth over $5,000, including:
+ Flagship benchmarks
+ Evaluations for popular software tools
+ Automated AI research tasks
+ Integrations with popular RL trainers
+ Major feature additions to Prime Intellect libraries
We're also looking for partners who want to sponsor environments.
E.g. If you:
+ Want envs for skills in specific domains
+ Want envs built around your tools
+ Want to show your evals on the Hub
+ Need custom RL envs for your own models
From autonomous AI research, MCP integrations, and browser automation to domain specific environments for economically valuable tasks across law, finance, and tax.
NanoGPT Speedrun
Evaluate code-generation and pretraining capabilities of LLMs via NanoGPT Speedrun benchmark.
- Request 8–1,000+ GPU clusters
- Get quotes from up to 50+ providers in 24h
- Re-sell idle GPUs back to our spot market
- Support from our research team
Expanding our Compute Exchange
- Find the best and most cost-effective reserved instance offers across 50+ providers
- Re-sell idle GPUs from your reserved cluster on our liquid compute market
- H100s, H200s, B200s, and NVL72 clusters available today
Additional Features
- Orchestration with SLURM, Ray or Kubernetes
- Monitoring with Grafana dashboards
- Native integrations into our full-stack infra offering: Environment Hub, Sandboxes, Reinforcement Fine-Tuning, Multi-Node Training
- Dedicated support from our research team
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.
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
Who should apply?
— Grad students with research ideas
— Independent builders & hackers
— Part time researchers exploring novel RL environments and evals
If you’ve wanted to build environments but lacked compute or support - this is for you
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.
This is why environments are pivotal to the next wave of AI progress.
But while big labs are spending millions buying and privatizing RL environments, open-source has no comparable way to crowd-source them at scale.
We’re building the platform and infrastructure to change that.
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.
Pipeline Parallelism
No single GPU holds the full model - each handles a stage, streaming activations forward. This lets smaller GPUs run large models like DeepSeek-R1. Hidden states pass stage to stage; the final GPU decodes a token, sends it back, and the cycle continues.