|
RL at 1T Scale: prime-rl Performance Deep Dive
|
Matej Sirovatka |
2026-06-21 |
2,292 |
--
|
|
Systematic Reward Hacking and Prime Sprints
|
Jessica Li |
2026-05-20 |
3,751 |
--
|
|
Introducing Lab: The Full-Stack Platform for Training your Own Models
|
Prime Intellect Team |
2026-02-10 |
1,660 |
--
|
|
Releasing Hosted Evaluations: Making benchmarking effortless
|
Florian Brand |
2026-05-28 |
964 |
--
|
|
Post-Training Nemotron 3 on Lab
|
Prime Intellect Team |
2026-06-04 |
1,016 |
--
|
|
$130M Series A to Build the Open Superintelligence Stack
|
Prime Intellect Team |
2026-07-08 |
627 |
--
|
|
Prime Intellect Joins the NVIDIA Nemotron Coalition to Advance Open Frontier Models
|
Prime Intellect Team |
2026-06-04 |
565 |
--
|
|
Launching FrontierSWE on the Environments Hub
|
Rajan Agarwal (Proximal) |
2026-04-16 |
757 |
--
|
|
renderers: Token-Level Templating for Agentic RL
|
Prime Intellect Team |
2026-05-12 |
3,305 |
--
|
|
Partnering with Browserbase to Train Browser and Computer Use Agents
|
Jessica |
2026-03-30 |
338 |
--
|
|
General Agent: A Self-Evolving, Synthetic Agent Environment
|
Mika |
2026-05-18 |
3,537 |
--
|
|
Recursive Language Models: the paradigm of 2026
|
Sebastian |
2026-01-01 |
7,194 |
--
|
|
Leveraging NVIDIA to Build the Open Superintelligence Stack
|
Prime Intellect Team |
2026-03-16 |
1,221 |
--
|
|
True Agents Model the World
|
Prime Intellect Team |
2026-06-05 |
4,897 |
--
|
|
Releasing Lab: the training platform for self-improving agents
|
Prime Intellect Team |
2026-05-07 |
839 |
--
|
|
prime-rl gets an Algorithms layer
|
Prime Intellect Team |
2026-07-05 |
1,493 |
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|
