# Launch finetuning on your dataset
python lerobot/scripts/train.py \
--policy.path=Beeface/smolvla-libero-spatial \
--dataset.repo_id=lerobot/svla_so101_pickplace \
--batch_size=64 \
--steps=20000 \
--output_dir=outputs/train/my_smolvla \
--job_name=my_smolvla_training \
--policy.device=cuda \
--wandb.enable=true# Run the policy using the record function
python -m lerobot.record \
--robot.type=so101_follower \
--robot.port=/dev/ttyACM0 \ # <- Use your port
--robot.id=my_blue_follower_arm \ # <- Use your robot id
--robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras
--dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording
--dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub
--dataset.episode_time_s=50 \
--dataset.num_episodes=10 \
--policy.path=Beeface/smolvla-libero-spatialSmolVLA Fine-tuned on LIBERO-Spatial
This is a fine-tuned version of lerobot/smolvla_base trained on the LIBERO-Spatial benchmark using the LeRobot framework.
Demo Video
Task 8 success episode (70% success rate on this task):
Model Details
- Base model: lerobot/smolvla_base
- Parameters: 450M total (100M trainable action expert)
- Training steps: 20,000
- Batch size: 8
- Hardware: NVIDIA L4 24GB (Google Colab Pro)
- Training time: ~2.5 hours
Performance on LIBERO-Spatial
| Task | Success Rate |
|---|---|
| task_0 | 60% |
| task_1 | 50% |
| task_2 | 60% |
| task_3 | 10% |
| task_4 | 20% |
| task_5 | 20% |
| task_6 | 10% |
| task_7 | 30% |
| task_8 | 70% |
| task_9 | 30% |
| Overall | 36% |
Training Command
lerobot-train \
--policy.type=smolvla \
--policy.pretrained_path=lerobot/smolvla_base \
--dataset.repo_id=HuggingFaceVLA/libero \
--batch_size=8 \
--steps=20000 \
--seed=42
Ablation Study — Training Duration
We evaluated checkpoints at multiple steps to understand convergence:
| Training Steps | Success Rate |
|---|---|
| 2,000 | 2% |
| 6,000 | 17% |
| 10,000 | 31% |
| 20,000 | 36% |
Performance improves consistently but with diminishing returns, suggesting convergence begins around 10K steps on LIBERO-Spatial.
Framework
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# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]