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uom-physical-ai/
calvin-openvla-oft-lora-epoch0

Name: calvin-openvla-oft-lora-epoch0
Availability: InStock

adversarial-robustness

openvla/openvla-7b

adapter

mit

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calvin-openvla-oft-lora-epoch0

OpenVLA-OFT (OpenVLA-7B LoRA) fine-tuned on CALVIN task_D_D — first post-epoch-0 checkpoint (step25212).

Fine-tuned with LoRA on CALVIN task_D_D for the paper:

"Reasoning as a Double-Edged Sword: Vulnerability and Defense in VLA Pipelines" NeurIPS 2026 — University of Melbourne Physical AI Group

Code and probe results: https://github.com/uom-physical-ai/calvin-vla-adversarial

Checkpoint details

Field	Value
Base model	`openvla/openvla-7b`
Fine-tune dataset	CALVIN task_D_D (512,077 episodes)
Step	25212
Epoch (training)	0
Adversarial probe step	25212

Adversarial probe results (Gaussian image noise, n=300, seed=42)

σ	mean_RAS	p95_RAS	mean_RS
0.05	0.1183	0.7054	0.2484
0.10	0.2211	1.2795	0.4487
0.20	0.3588	1.5071	0.6699

RAS = Relative Action Shift (L2 deviation of predicted action, normalised). Lower = more robust.

Usage

python

from peft import PeftModel
from transformers import AutoModelForVision2Seq, AutoProcessor

base = AutoModelForVision2Seq.from_pretrained("openvla/openvla-7b")
model = PeftModel.from_pretrained(base, "uom-physical-ai/calvin-openvla-oft-lora-epoch0")
processor = AutoProcessor.from_pretrained("openvla/openvla-7b")

See the GitHub repo for the full fine-tuning and probe pipeline.