VGAS-5-shot / README.md
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---
license: mit
library_name: lerobot
tags:
- robotics
- vision-language-action
- libero
- smolvla
- vgas
---
# VGAS and VGAS+ — 5-Shot LIBERO Checkpoints
This repository contains the checkpoints used to evaluate
[VGAS](https://arxiv.org/abs/2602.07399) and VGAS+ on the 5-shot LIBERO benchmark.
The implementation is available in the
[VGAS code repository](https://github.com/Jyugo-15/VGAS).
## Repository layout
```text
smolvla/5_SHOT/pretrained_model/ # Shared 5-shot SmolVLA policy
{suite}/vgas_critic/last.ckpt # VGAS inference-time critic
{suite}/vgas_plus/pretrained_model/ # Distilled VGAS+ policy
```
Here, `{suite}` is one of `goal`, `object`, `spatial`, or `long` (`long` corresponds
to `libero_10`). VGAS uses the shared SmolVLA policy together with the suite-specific
critic for Best-of-N selection. The SFT policy is frozen while training the VGAS critic.
VGAS+ directly executes the distilled policy and does not require a critic or
inference-time reranking.
## VGAS+ checkpoints
| Directory | LIBERO suite |
|---|---|
| `goal/vgas_plus/pretrained_model` | `libero_goal` |
| `object/vgas_plus/pretrained_model` | `libero_object` |
| `spatial/vgas_plus/pretrained_model` | `libero_spatial` |
| `long/vgas_plus/pretrained_model` | `libero_10` |
Download one policy with `huggingface_hub`:
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="SemyonXu616/VGAS-5-shot",
allow_patterns=["goal/vgas_plus/pretrained_model/*"],
local_dir="checkpoints/VGAS-5-shot",
)
```
The downloaded policy directory can be passed directly as `POLICY_PATH` to the
VGAS+ evaluation scripts in the code repository.
## Citation
```bibtex
@article{xu2026vgas,
title = {VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation},
author = {Xu, Changhua and Yu, En and Xuan, Junyu and Lu, Jie},
journal = {arXiv preprint arXiv:2602.07399},
year = {2026}
}
```
The VGAS+ citation will be added when its preprint is available.