Principle:Alibaba ROLL Agentic Validation
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement_Learning, Evaluation, Agentic_AI |
| Last Updated | 2026-02-07 20:00 GMT |
Overview
An evaluation principle for assessing LLM agent performance on validation environments at training checkpoints.
Description
Agentic Validation evaluates the trained policy by running complete rollouts on held-out validation environments and computing per-environment score statistics. Unlike training rollouts, validation uses a separate RolloutScheduler and dataset, and collects metrics without gradient computation.
The validation provides:
- Aggregate scores: Mean, max, min across all validation episodes
- Per-environment scores: Breakdown by environment tag (e.g., Sokoban, FrozenLake)
- Score history: Tracking performance across training steps
Usage
Use this principle at configured evaluation intervals (eval_steps) during agentic RL training to monitor training progress and detect overfitting.
Theoretical Basis
Validation performance is measured as the expected return under the current policy on unseen environment instances.
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