s1ghhh/VLADrop_pi05_LIBERO_Drop9MHA_Uniform

VLADrop-pi05-LIBERO-mha-only-drop9

Checkpoint for Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?.

DTR (Drop-Then-Recovery) removes transformer blocks from a pretrained VLA model and recovery-fine-tunes the smaller dense model. Code: https://github.com/s1ghhh/VLADrop

This checkpoint

Paper row	Table 2: pi0.5 MHA (granularity study)
Dropped blocks	Language backbone (PaliGemma, 18 layers): drop only the MHA sublayers of the 9 odd-indexed blocks [1,3,5,...,17]. MLP sublayers kept.
Recovery training	batch size 32, 30K steps, lr 5e-5
LIBERO success rate	Spatial 96.4 / Object 98.0 / Goal 95.8 / Long 86.2 / Avg 94.1

Usage

This is an openpi-format pi0.5 checkpoint (PyTorch). Use with the VLADrop fork: https://github.com/s1ghhh/VLADrop

python scripts/serve_policy_batch_drop.py \
    --config pi05_libero_dropped \
    --dir <this_repo_local_path> \
    --port 8000

Important: the drop lists are NOT stored inside the checkpoint. Pass the exact llm_drop_attn_list / llm_drop_mlp_list shown above (via config or CLI) when serving, otherwise layers will be mismatched. assets/ contains the LIBERO norm stats. The optimizer state (train_state/) is not included.

Citation

@article{sun2026vladrop,
  title={Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?},
  author={Sun, Guoheng and Feng, Kaixi and He, Shwai and Gong, Xiaochuan and He, Yexiao and Wang, Ziyao and Shen, Zheyu and Ye, Wanghao and Kompella, Ramana Rao and Liu, Gaowen and Li, Ang},
  journal={arXiv preprint arXiv:2606.27755},
  year={2026}
}