Config Reference
Training is configured via YAML files in examples/train/. All values can be
overridden on the CLI by appending key=value arguments.
Key configs
| File | Description |
|---|---|
examples/train/pi05/cloud.yaml |
π₀.₅ LoRA, cloud/HPC (recommended) |
examples/train/pi05/cloud_full.yaml |
π₀.₅ full fine-tuning via FSDP |
examples/train/pi05/async_lora.yaml |
π₀.₅ LoRA, local install |
examples/train/pi0/async_lora.yaml |
π₀ LoRA |
Important fields
policy:
type: pi05 # pi05 or pi0
pretrained_path: lerobot/pi05_base # HF Hub ID or local path
push_to_hub: false # set true to auto-upload checkpoint
repo_id: your-org/your-model # required when push_to_hub: true
dtype: bfloat16
gradient_checkpointing: false # enable to save VRAM at ~20% speed cost
dataset:
repo_id: ${DATASET_REPO_ID} # HF Hub ID or absolute local path
root: /scratch/.cache/lerobot # local cache location
video_backend: torchcodec # torchcodec (GPU) or pyav (CPU/local)
output_dir: /scratch/outputs/pi05_cloud
batch_size: 1
grad_accum_steps: 8 # effective batch = batch_size × NUM_GPUS × grad_accum_steps
steps: 50000
num_workers: 4
max_delay_steps: 8 # temporal delay augmentation; 0 = sync training
shared_observation: true # ~9× training speedup when max_delay_steps > 0
lora:
enable: true # false = full fine-tuning (use TRAIN_BACKEND=fsdp)
r: 16
alpha: 16
use_qlora: false # 4-bit quantised LoRA (fits in 8 GB)
wandb:
enable: false # set true + export WANDB_API_KEY to enable
project: vlash