Principle:CarperAI Trlx Evaluation Metrics Design
| Knowledge Sources | |
|---|---|
| Domains | Evaluation, NLP, Reinforcement_Learning |
| Last Updated | 2026-02-07 16:00 GMT |
Overview
A design principle for creating evaluation metric functions that monitor language model quality during RL or SFT training.
Description
During training, periodic evaluation provides insight into model behavior beyond the training loss or reward signal. Evaluation metric functions generate text from held-out prompts and compute quality metrics on the generated outputs. Unlike reward functions (which drive optimization), metric functions are observational — they log statistics for monitoring without influencing gradient updates.
In trlx, the metric function is called during evaluation intervals on batches of generated text. It returns a dictionary mapping metric names to per-sample scores, which are then logged to trackers (Weights & Biases, TensorBoard). This allows tracking multiple dimensions of quality simultaneously (e.g., sentiment, fluency, diversity).
Usage
Design a metric function when you need to monitor generation quality during training beyond the primary reward signal. Pass it as the metric_fn argument to trlx.train(). Metric functions are particularly important for offline training (ILQL, SFT) where there is no live reward function, and for detecting reward hacking in PPO.
Theoretical Basis
Evaluation metrics serve as a multi-dimensional assessment:
Where each metric measures a different quality dimension.
Design principles:
- Independence from reward: Metrics should measure aspects not captured by the reward signal
- Interpretability: Each metric should have a clear meaning (e.g., "sentiment score", "ROUGE-L")
- Per-sample granularity: Return one value per sample for detailed analysis
- Efficiency: Called periodically, so batch processing is preferred