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lerobot/smolvla_base on
Mehul2112/so100_black_cube_merged
(SO-100, single front camera, 130 episodes).train_expert_only=true, freeze_vision_encoder=true).| Step | Train loss | Eval loss (15% held-out) |
|---|---|---|
| 2000 | 0.179 | 0.319 ← eval minimum |
| 4000 | 0.121 | 0.348 |
| 6000 | 0.090 | 0.418 |
Note: Eval loss reached its minimum at step 2000 and rose afterward (overfitting on a small 110-episode training split). The step-6000 checkpoint here has the lowest training loss but is past the generalization optimum; for best real-robot generalization, the step-2000 checkpoint is preferable.
1from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
2policy = SmolVLAPolicy.from_pretrained("Mehul2112/smolvla_black_cube_expert")