Daytona teamed up with HUD Evals to release a practical RL cookbook, demonstrating how scaling from local prototyping to Daytona sandboxes boosted held-out bug-fix accuracy from 35.9% to 81.2% in 10 RL steps.
Key Takeaways
- ✓Democratizes reinforcement learning by delivering a reproducible pipeline from laptop to cloud sandboxes;
- ✓Maintained 100% reliability across 1,272 ephemeral sandbox lifecycles under intensive RL evaluation loops;
- ✓Drives a massive 45.3 percentage-point improvement in held-out coding benchmark performance.