design-environment

To build a reinforcement learning environment for custom assistant training, you define a BaseEnv subclass in run.py with a clear system prompt, discriminating reward logic, and correct dataset preprocessing. This ensures your training job is validated and ready for reliable runs.

A discriminating reward in reinforcement learning environment design is validation-friendly reward logic that accurately differentiates successful model outputs from failures. It is essential for setting up reliable training runs and ensuring correct behavior during single-turn or multi-turn rollouts.

Yes, you can set up multi-turn training environments for tool use and RAG search. The environment design supports multi-turn setups for tool-driven tasks, retrieval-augmented generation, and trace imitation by configuring the correct tool surface and dataset preprocessing in your BaseEnv subclass.

Yes, this approach supports both single-turn training jobs and trace imitation. You can configure your custom run.py environment to handle single-turn assistant tasks or build multi-turn setups specifically designed for imitating traces with validated dependency handling.

The best way to validate tool handling and dependencies in an RL training environment is to implement correct tool or dependency handling within your BaseEnv subclass. This ensures training readiness and prevents rollout failures during tool-driven multi-turn tasks.

Your RL training job requires dataset preprocessing before launch to ensure training readiness and validation-friendly behavior. Correct preprocessing aligns the data with your discriminating reward structure, guaranteeing reliable rollout flows for single-turn and multi-turn tasks.