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

# Debugging Training

> Common errors encountered during training runs and how to handle them

When running large-scale RL training against OpenReward environment endpoints, you may encounter errors related to capacity limits or resource constraints. This page covers the most common ones and how to handle them.

## Max capacity errors

During training you may see errors like this in your rollout logs:

```
ClientResponseError(429): Environment at maximum capacity.
Ask the environment owner to increase the max pods or sessions per pod.
```

This means the environment can't handle the number of concurrent sessions your training run is requesting. There are two ways to address this:

1. **Increase capacity on the environment.** If you own the environment (or can contact the owner), increase the max pods or sessions per pod in the environment's settings.

2. **Lower your max concurrency.** Reduce the max concurrency in your training settings so fewer sessions are requested at once.

## Memory allocation errors

You may also see errors like this:

```
ClientResponseError(503): Pod unavailable: Container ran out of memory.
Ask the environment owner to increase the memory allocation.
```

This means the environment server itself crashed due to OOM during your rollouts. Unlike a max capacity error, this requires the environment to restart before it can serve requests again. To handle this:

1. **Retry the affected rollouts.** Your training code should detect these failures and redo the rollouts once the environment server comes back up.

2. **Reduce memory pressure on the environment.** This is the longer-term fix. See [Out of memory (OOM) crashes](/environments/debugging-environments#out-of-memory-oom-crashes) in the environment debugging guide for specific strategies - loading less data, using index-based task access, or increasing the environment's memory allocation.