> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tuneplane.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Review

> Overlay several runs and decide which one to keep.

Pick two or more jobs and the console overlays their curves, diffs their configs, and shows a
scorecard. The question it is built to answer is not "what happened" but "did this get better, and
which run do we keep".

![TunePlane review page](https://mintcdn.com/starforge/GatXR2rI5-_Vm4_H/images/console/review.png?fit=max\&auto=format\&n=GatXR2rI5-_Vm4_H\&q=85\&s=c0f89b6e81c660ea39a15053b551c9a9)

Review: overlaid curves, a scorecard, and the config diff explaining why they differ.

## What you see

| Region | What it shows |
| - | - |
| **Scorecard** | Benchmark scores side by side, with regressions marked by each metric's declared direction |
| **Primary curves** | Validation accuracy and training reward, overlaid |
| **Config diff** | Only the keys that actually differ, so a fifty-key config reduces to the three you changed |
| **Ask Agent** | Ask in plain language whether the runs improved and which to keep |

The config diff is the part people underuse. Two runs that disagree on a curve almost always
disagree on a config key, and the diff finds it faster than reading two YAML files.

## What you can do

* **Add or remove runs** — the overlay updates without reloading.
* **Ask Agent** — natural-language analysis of the selected runs. Locked to administrators unless
  the deployment sets `TUNEPLANE_PREMIUM_PUBLIC`.
* **Share** — a read-only link to this exact comparison, with an expiry. See
  [sharing](https://docs.tuneplane.ai/en/guides/sharing.md).

## Reading a comparison honestly

> **Identical configs, different curves**
>
> Seeds. Post-training runs vary between identical configurations, sometimes considerably. Two runs
> is not a result; if the difference matters, run the variant again.

> **Better on the primary metric, worse elsewhere**
>
> The normal outcome, and the reason the scorecard shows several benchmarks rather than one. Post-
> training reliably improves one axis while quietly damaging another — that is what the math, code,
> knowledge, Chinese and instruction-following split exists to make visible.

> **Reward climbing, benchmark flat**
>
> The model is getting better at your reward, not at the task. Worth looking at the validation
> samples before celebrating: reward hacking looks exactly like this from the curve alone.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.