Scaling Agentic RL: 365,000+ Environments for SWE, Terminal, and Search
Blog post from Prime Intellect
The open research ecosystem has developed numerous datasets for software engineering, terminal use, and web research, each with unique harnesses, image conventions, and grading scripts, leading to challenges in integration and evaluation. To address this, an integrated system has been introduced that consolidates 23 tasksets under a unified API, allowing for consistent evaluation and reinforcement learning (RL) training across approximately 365,000 tasks. This integration maintains the original grading paths of each taskset while normalizing them around a single API, ensuring that the original scoring semantics remain intact. The tasks are organized into three main domains: software engineering, terminal, and search, with a focus on creating a seamless and scalable environment for RL training. To ensure the integrity and reliability of the datasets, a rigorous validation process is employed, filtering out tasks with broken images, unstable tests, or solvable without intended code interaction. This consistent validation and re-upload process aims to maintain high standards and transparency, with validated datasets available for public use, enhancing the accessibility and reproducibility of RL experiments in agentic domains.
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