July 2026 Summaries

The company has announced a $130 million funding round led by Radical Ventures, with contributions from NVIDIA Ventures, Intel Capital, Dell Technologies Capital, and existing investors, bringing its total funding to over $150 million to develop the open superintelligence stack. This funding will help scale its infrastructure, allowing companies to own their model optimization loop and build continuously improving AI agents. The open superintelligence stack encompasses training, deploying, and refining models, and is already used by over 6,000 customers, generating more than $100 million in annualized revenue. Notable success stories include Ramp, which developed a model that outperformed closed frontier models in efficiency and cost. The company is focusing on scaling compute clusters, enhancing reinforcement learning (RL) capabilities, and advancing long-horizon agents and Recursive Language Models (RLMs) to address complex AI challenges. They are actively hiring to expand their team and further develop infrastructure that supports open superintelligence initiatives.

Prime-rl has introduced a first-class algorithms layer designed to centralize algorithm-specific elements within its reinforcement learning framework, which now supports six built-in algorithms—GRPO, MaxRL, On-Policy Distillation, Self-Distillation, SFT distillation, and ECHO. This new layer allows for algorithm selection on a per-environment basis, enabling a single run to train different algorithms on different environments without altering the core trainer, thus enhancing flexibility and performance. The centralization is achieved by organizing each algorithm as a module under a common orchestrator directory, simplifying the understanding and modification of algorithms without compromising system performance. This setup allows researchers to innovate by subclassing a common abstraction without delving into trainer internals and supports multi-teacher distillation, enabling environments to utilize specialized teacher models for more domain-specific training. By resolving algorithms per environment rather than per run, prime-rl offers a unique approach not commonly found in other open-source RL frameworks, facilitating more targeted and effective training signals based on the specific properties of each environment.