> For the complete documentation index, see [llms.txt](https://slm-lab.gitbook.io/slm-lab/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://slm-lab.gitbook.io/slm-lab/using-slm-lab/resume-and-replay.md).

# Resume and Replay

Resume interrupted training or replay trained models.

## Resume Training

Use `train@{predir}` to resume from a previous run:

```bash
# Resume from latest run of this spec
slm-lab run slm_lab/spec/benchmark/ppo/ppo_cartpole.json ppo_cartpole train@latest

# Resume from specific folder
slm-lab run slm_lab/spec/benchmark/ppo/ppo_cartpole.json ppo_cartpole train@data/ppo_cartpole_2026_01_30_221924
```

`train@latest` resolves to the most recent `data/{spec_name}_*/` folder.

### Extending Training

To continue a completed run (e.g., 100k → 200k frames):

1. Edit the spec's `max_frame`
2. Resume with `train@latest`

## Replay Mode

Use `enjoy@{spec_file}` to replay a trained model with rendering:

```bash
slm-lab run slm_lab/spec/benchmark/ppo/ppo_cartpole.json ppo_cartpole enjoy@data/ppo_cartpole_2026_01_30_221924/ppo_cartpole_t0_spec.json
```

In enjoy mode, the spec file and spec name args are ignored—everything loads from the `enjoy@` path.

Enjoy mode finds the best session (by `total_reward_ma`) and loads its `ckpt-best` model checkpoint.

## Replaying Published Benchmarks

Download and replay trained agents from [HuggingFace](https://huggingface.co/datasets/SLM-Lab/benchmark):

```bash
slm-lab list              # List available experiments
slm-lab pull ppo_cartpole # Download trained model
slm-lab run slm_lab/spec/benchmark/ppo/ppo_cartpole.json ppo_cartpole enjoy@data/ppo_cartpole_2026_01_30_221924/ppo_cartpole_t0_spec.json
```

See [Public Benchmark Data](/slm-lab/benchmark-results/public-benchmark-data.md) for the full list.