---
title: Interpreting results | Adaption
description: Retrieve an AutoScientist run and understand its win rate and training diagnostics.
---

## Retrieve a completed run

Use the run ID returned when you [create the run](/api/python/resources/autoscientist/methods/create/index.md), then [retrieve its current results](/api/python/resources/autoscientist/methods/get/index.md):

```
from adaption import Adaption


client = Adaption()


run_id = "autoscientist_run_abc123"
run = client.autoscientist.get(run_id)


if run.status in {"pending", "running"}:
    run = client.autoscientist.wait_for_completion(run.id)


if run.status == "failed":
    raise RuntimeError(f"failed: {run.error}")
if run.status != "succeeded":
    raise RuntimeError(f"Run ended with status: {run.status}")


print(f"Iterations: {run.iterations_completed}/{run.max_iterations}")
print(f"Best win rate: {run.best_win_rate}")
print(f"Checkpoint available: {run.download_available}")
```

The app’s results page also shows the trained model ID, **Weights export**, and **AutoScientist Config**. Response fields can evolve; use the [`get` API reference](/api/python/resources/autoscientist/methods/get/index.md) for the exact SDK field that corresponds to an app value.

## Training win rate

The **Training Winrates** chart compares the base model with the adapted model on evaluations derived from your dataset. A 70% adapted-model win rate means the adapted model won roughly 70% of the head-to-head comparisons.

`best_win_rate` is the best result across all iterations. A `succeeded` run may have reached `target_win_rate` early or simply completed `max_iterations`, so compare those values rather than treating success as proof that the target was reached.

## Train and evaluation metrics

The charts plot standard diagnostics over training steps:

- **Loss** measures prediction error. Falling training and evaluation loss generally indicates learning; falling training loss paired with worsening evaluation loss can indicate overfitting.
- **Learning rate** shows the optimizer’s step size over time and reflects the configured warmup and schedule.
- **Gradient norm** measures the overall gradient magnitude. Sudden spikes can indicate unstable updates, while a stable curve suggests optimization has settled.

Read these curves together. A smooth loss curve is not enough if win rate is flat, and a strong win rate can still accompany unstable training diagnostics that deserve investigation.

## Common issues

- **Flat or negative win rate:** use a model with enough capacity, consider more epochs, and evaluate whether [augmentation](/autoscientist/data-augmentation/index.md) should bring the domain dataset toward 20,000 rows.
- **No downloadable checkpoint:** confirm the run succeeded and `download_available` is true before following [Download the model](/autoscientist/download-the-model/index.md) or calling the [`download` method](/api/python/resources/autoscientist/methods/download/index.md).
- **Unclear run state:** retrieve the run and inspect `status`; `pending` is queued, `running` is active, and `failed` includes the reason in `error`.
- **Need original rather than adapted rows:** create a [raw dataset](/autoscientist/run-on-non-adapted-data/index.md) or select original columns when configuring the run.