Prime Intellect

reinforcement learning with verifiable rewards on LLM is one of the most interesting breakthrough of 2025 imo being able to fine tune an LLM to reach SOTA result was something that was almost exclusively the domain of big labs. but deepseek r1 showed that with GRPO and a proper reward functions you could steer a model towards superior result with a relatively simple methodology. 10 months later finetuning an LLM with RL is much simpler and even work on small models we initially thought couldn’t benefit from it. the hard part, like anything RL related, stays the proper specification of the environment and the rewards functions. soooo I did a deep dive on RLVR environment with my friends from Prime Intellect lots of fun we leveraged the open source library verifiers for most of the example check it out.

Prime Intellect's Inference API is live. Use it to evaluate environments on the Hub. + Simple `prime env eval` cmd + One endpoint for all models + 56 models and growing + Unified billing with compute + Viewer for rewards and rollouts + Share results on Hub View all supported models using `prime inference models` Full docs on our Inference API: https://lnkd.in/ggk5SKYM Full docs on evaluating environments on our Hub: https://lnkd.in/gufsaQgQ

Introducing Michael Keiblinger's work on "Recurrence-Complete Frame-based Action Models" A paper on why long-horizon perception requires rethinking recurrence.

With the US falling behind on open source models, one startup has a bold idea for democratizing AI: let anyone run reinforcement learning.

We present to you the unified Prime Intellect infrastructure stack Now available on GitHub https://lnkd.in/gqj-F82b

Excited to share: I’ve uploaded my first RL environment to Prime Intellect’s Environments Hub - med-spell-count-qa. It’s a lightweight, objective task for GRPO training: count specific letters in real medical terms. Paired with the MedSpellCountQA dataset on Hugging Face, it’s a clean benchmark for string-reasoning and attention under RL. If you’re exploring GRPO or want fast, reproducible RL evals beyond math-only tasks, this is for you. 🚀

We are launching Reserved Instances. Prime Intellect’s internal research team has benefited from using our global network of cloud partners to secure GPU clusters, dynamically monetizing idle compute in our ecosystem, and mitigating risk as we scale. Now we’re providing this capability to the broader community. Prime Intellect reserved instances transform how GPUs are bought, used, and sold. Request 8–1,000+ GPU clusters and receive quotes from 50+ providers in under 24 hours. Receive immediate access to secure H100s, H200s, B200s, and NVL72 clusters. Monetize idle reserved GPUs through our spot market, reducing costs for reserved instance buyers, spot users, and providers. Additional Features - Monitoring with Grafana dashboards - Dedicated support from our research team - Orchestration with SLURM, Ray or Kubernetes - Native integrations: Environment Hub, Sandboxes, RFT, Multi-Node Training Submit a request today: https://lnkd.in/gtHtxwgw

For over a year now I've been the sole growth personnel at Prime Intellect. A lot has changed since I first started but the goal remains the same, create open source intelligence. Unlike traditional growth roles focused on enabling sales at Prime Intellect growth enables research. From our own lab's research to external labs using our ◦ GPU Cloud Marketplace ◦ Distributed Training ◦ Environment Hub Today Prime Intellect is hiring for three roles on the growth team. We are looking for individuals who understand the risks of centralized super intelligence and have deep expertise for their respective roles to drive change. If this is you or someone you know, please see the 3x jobs linked below ↓