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Axol records every episode in the LeRobot dataset format, so training a policy is just standard LeRobot training — there’s nothing Axol-specific about it. Once you’ve recorded a dataset with Data Collection, you point LeRobot’s training script at it, and the resulting checkpoint runs back on the robot with Run Policy.
Because the data is plain LeRobot format, any LeRobot training tutorial applies directly — just substitute your own dataset repo_id. The links below are the best places to start.

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Which policy to train

ACT (Action Chunking with Transformers) is the recommended starting point: it’s lightweight (~80M params), trains in a few hours on a single GPU, and often reaches high success rates with as few as ~50 demonstrations. Once you have a baseline working, you can explore larger vision-language-action models like SmolVLA.

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Train it

With the lerobot extra installed on a machine with a CUDA-capable GPU, train directly from your dataset’s Hub repo_id:
Training writes checkpoints under outputs/train/.../checkpoints, and (with --policy.repo_id) pushes the trained policy to the Hub so it’s reachable from the robot machine and any inference server. Expect a few hours for ~100k steps on a single GPU.
No local GPU? The ACT tutorial links a Google Colab notebook you can use to train in the cloud.

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LeRobotTutorials

ACT

The recommended first policy — architecture, training, and tips.

Imitation learning walkthrough

End-to-end record → train → evaluate with lerobot-train.

SmolVLA

Fine-tune Hugging Face’s lightweight vision-language-action model.

LeRobot docs

Full documentation, models, and datasets.

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Next steps

Run Policy

Deploy your trained checkpoint on the robot.

Data Collection

Record more episodes to improve the policy.