rl-experiment-plan

Prevent premature reinforcement-learning training by formalizing your experiment intent into a scoped contract that defines metrics, guardrails, compute budget, and approval state before execution. This structured gating ensures you avoid wasting compute on poorly scoped tasks.

An RL experiment contract is an explicit record of scope, primary metrics, tuning limits, hardware targets, and evaluation rules. You need one before launching any compute-heavy reinforcement-learning training to establish baseline planning and safety gates.

Plan baselines and evaluation runs by defining a baseline command and evaluation protocol within an approved experiment state. This process prepares smoke and evaluation runs without altering existing reward logic, curriculum, domain randomization, or task code.

Yes, you can update an active study contract to adjust safety gates, tuning scope, and compute budget. This update process explicitly maintains baseline planning and evaluation protocols without changing reward logic, curriculum, or domain randomization.

Approval gating requires explicit confirmation of task definition, primary metric, guardrails, tuning scope, compute budget, hardware target, baseline command, and evaluation protocol. You cannot launch training execution until this approved experiment state is fully defined and confirmed.