prepare-verifier-handoff

Hosted RL training requires a deterministic environment with reset and step functions, packaged alongside conformance checks and a return-eval harness to preserve holdout boundaries during partner handoff.

You can determine if your workload needs hosted reinforcement learning by applying gate-based decisioning to confirm RL necessity and verify the workload is a fit for hosted training before beginning the handoff process.

Environment inversion in RL handoffs refers to structuring a deterministic training environment so a partner trainer can drive work through standardized reset and step cycles while maintaining holdout boundaries and conformance checks.

Yes, you can package a deterministic RL environment for Prime Intellect Verifiers by building a conformance check during the packaging stage, ensuring the environment meets verification standards before partner handoff.

The RL handoff workflow involves three stages: authoring a deterministic RL environment, packaging the environment with conformance checks for verifiers, and preparing a handoff artifact with a return-eval harness for hosted training.

Conformance checks are needed for RL environment handoffs to verify that the packaged environment meets standards and preserves holdout boundaries, ensuring the partner receives a valid, testable training artifact.