VLADrop: Drop-Then-Recovery (DTR) Checkpoints
Collection
Checkpoints for 'Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?' (arXiv:2606.27755). Code: https://github.com/s1ghhh/VLADrop • 64 items • Updated
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VLADrop Calibration Data
Calibration sets for the layer-importance profiling in Drop-Then-Recovery (DTR): How Redundant Are Vision-Language-Action Models? (paper · code · checkpoints).
Each file holds the exact 512 samples (64 batches × 8) a profiling run consumes — not the full dataset. Loading these reproduces the paper's GateProbe / baseline-metric block rankings exactly.
| File | Setting | Seed |
|---|---|---|
pi05_libero_dropped_calib_64x8_seed42.pt |
pi0.5 × LIBERO | 42 |
pi05_libero_plus_calib_64x8_seed42.pt |
pi0.5 × LIBERO-Plus | 42 |
openvla_libero_calib_64x8_seed9999.pt |
OpenVLA-OFT × LIBERO | 9999 |
openvla_libero_plus_calib_64x8_seed9999.pt |
OpenVLA-OFT × LIBERO-Plus | 9999 |
pi0.5 calibration is stored in bfloat16 (pi0.5's runtime precision) and is regenerated
deterministically by profiling/pi0.5/dump_calib.py in the code repo.
Usage
huggingface-cli download s1ghhh/VLADrop_Calibration_Data --repo-type dataset \
--local-dir profiling/calibration_data
See profiling/README.md in the code repo for the full
reproduction commands.
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