reset / step (and optionally evaluate), so any environment speaking the
protocol can serve any trainer.
Miles integrates OpenEnv as an
agent-function integration: a Miles-side agent
function drives the agentic loop — reset(task_id), repeated steps, then
scoring the episode with the task’s own tests — against an unmodified OpenEnv
server, and the score becomes the sample’s reward through a custom reward
hook.
Try it
The maintained end-to-end recipe is Terminal-Bench-2 GRPO inexamples/experimental/openenv.
It runs against a shared Docker env server (full per-task image fidelity) or
per-episode Daytona cloud sandboxes built from each
task’s official image (no resident infrastructure). Follow the
recipe README
for prompt-data preparation, env-server modes, launcher flags, and operational
notes.