Principle:Alibaba ROLL SFT Validation
| Knowledge Sources | |
|---|---|
| Domains | Supervised_Learning, Evaluation |
| Last Updated | 2026-02-07 20:00 GMT |
Overview
An evaluation principle for monitoring SFT training progress by computing validation loss on held-out data.
Description
SFT Validation evaluates the model on a held-out dataset by computing cross-entropy loss without gradient computation. It monitors training progress and detects overfitting.
Usage
Use at configured intervals during SFT training.
Theoretical Basis
Validation loss on held-out data is the standard metric for supervised fine-tuning quality.
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