Cerebras

What this post added

The post describes refactoring the OpenHands evaluation workflow to dynamically load arbitrary Docker images and parse metadata at runtime, enabling support for thousands of diverse Python repositories. It also introduces a unified rollout architecture that maintains a persistent runtime context, eliminating container duplication and reducing overhead by approximately 95% for large-scale RL training. This enables multi-turn interaction support, fast rollout generation, sparse reward evaluation, and comprehensive trajectory logging.