---
id: assets.weights
title: Weights Training
summary: Prepare benchmark-guided training data, inspect coverage and splits, download exact packages, and follow training and evaluation results.
kind: reference
product_area: weights_training
status: stable
updated: 2026-10-30
canonical: /docs/weights-training
---

# Weights Training

Weights training turns selected Benchmark criteria into inspectable training material and connects that material to model training. Open it from the Benchmark workspace.

## Definition

A preparation records the Benchmark Version, training method, selected scope and generation settings. The workspace has **Overview**, **Dataset**, **Coverage** and **Results**.

Overview summarizes the preparation and prepares the exact dataset download. Dataset exposes examples and splits. Coverage shows representation. Results configures and follows connected training and separately authorized evaluation.

## Fields, states, or lifecycle rules

- Demonstration and preference datasets have different learner targets.
- Examples retain their governing criteria and source context.
- Training, validation and test splits remain distinguishable.
- A download package captures an exact dataset state and export profile.
- A training run records the connection, base model, configuration, provider job and resulting model identity.
- Training completion does not establish benchmark improvement.

## Use the workspace

1. Create a preparation for the intended Benchmark Version and method.
2. Inspect generated examples in Dataset.
3. Check splits and meaningful representation in Coverage.
4. Use **Prepare download** in Overview and inspect the package settings.
5. Download the exact ready package or use Results to configure a connected training service.
6. Launch training deliberately and follow the recorded job.
7. Authorize evaluation separately and inspect the actual resulting responses and judgments.

A provider connection is not required to download a canonical dataset. Connected training services accept only the methods, model families and material types supported by their selected profile.

## Keep training and evaluation separate

Generation checks describe prepared material. Coverage describes its representation. A provider completion identifies a training output. Held-out evaluation measures that output through its supported inference path. None substitutes for the others.

Keep test records out of training and provider validation uploads. Preserve exact package and model identities when comparing results with an ordinary Harness evaluation.
## Related task pages

{% related-card-grid title="Related task pages" %}
- [Training Datasets and Coverage](/docs/weights-training/datasets)
- [Export, Train, and Evaluate](/docs/weights-training/exports-and-training)
- [Workspace Integrations](/docs/admin-console/integrations)
- [Evaluation Results](/docs/benchmark-evaluations/results)
{% /related-card-grid %}