README.md

---
library_name: lerobot
license: apache-2.0
language:
  - en
base_model:
  - SberRoboticsCenter/GreenVLA-5b-base-stride-4
pipeline_tag: robotics
tags:
  - robotics
  - vla
  - vision-language-action
  - manipulation
  - flow-matching
  - action-prediction
  - green-vla
  - calvin
  - reinforcement-learning
model-index:
  - name: GreenVLA-5b-stride-4-R2-calvin
    results:
      - task:
          type: robotics
          name: CALVIN
        metrics:
          - type: avg_chain_length
            name: Avg Chain Length
            value: 4.57
---

<div align="center">

# GreenVLA-5b-stride-4-R2-calvin

### RL-Aligned VLA for CALVIN

**Sber Robotics Center &middot; Manipulation Team**

[![arXiv](https://img.shields.io/badge/arXiv-2602.00919-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.00919)
[![Project Page](https://img.shields.io/badge/Project-Page-blue?style=for-the-badge&logo=github&logoColor=white)](https://greenvla.github.io/)
[![Code](https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/greenvla/GreenVLA)

</div>

---

## Overview

**GreenVLA-5b-stride-4-R2-calvin** is the R2 (RL-aligned) checkpoint of the [Green-VLA](https://arxiv.org/abs/2602.00919) family, fine-tuned for the CALVIN benchmark environment.

Starting from [GreenVLA-5b-base-stride-4](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base-stride-4), this model went through both R1 (supervised fine-tuning) and R2 (RL policy alignment) stages on CALVIN data.

## Evaluation

Evaluated on the **CALVIN** benchmark:

| Metric | Value |
|--------|:---:|
| **Avg Chain Length** | **4.57** |

## Training

| | Details |
|---|---|
| **Base checkpoint** | [GreenVLA-5b-base-stride-4](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base-stride-4) |
| **Stage** | R2 — RL policy alignment |
| **Method** | Trajectory optimization (SFT + RL on collected trajectories) |
| **Environment** | CALVIN |
| **Parameters** | ~5B |

## Quick Start

### Installation

```bash
git clone https://github.com/greenvla/GreenVLA.git
cd GreenVLA
uv sync  # or: pip install -e .
```

### Inference

```python
import numpy as np
import torch
from lerobot.common.policies.factory import load_pretrained_policy
from lerobot.common.utils.torch_observation import (
    move_dict_to_batch_for_inference,
    torch_preprocess_dict_inference,
)

# 1. Load policy and transforms.
policy, input_transforms, output_transforms = load_pretrained_policy(
    "SberRoboticsCenter/GreenVLA-5b-stride-4-R2-calvin",
    data_config_name="calvin",
)
policy.to("cuda").eval()

# 2. Build an observation (replace with real sensor data).
raw_obs = {
    "observation/state": np.random.rand(8).astype(np.float32),
    "observation/image": np.random.randint(0, 256, size=(224, 224, 3), dtype=np.uint8),
    "prompt": "open the drawer",
}

# 3. Transform, preprocess, and batch.
obs = input_transforms(raw_obs)
obs = torch_preprocess_dict_inference(obs)
batch = move_dict_to_batch_for_inference(obs, device="cuda")

# 4. Predict actions and post-process.
with torch.inference_mode():
    raw_actions = policy.select_action(batch).cpu().numpy()

actions = output_transforms(
    {"actions": raw_actions, "state": batch["state"].cpu().numpy()}
)["actions"]
```

## Citation

```bibtex
@misc{apanasevich2026greenvlastagedvisionlanguageactionmodel,
    title   = {Green-VLA: Staged Vision-Language-Action Model for Generalist Robots},
    author  = {I. Apanasevich and M. Artemyev and R. Babakyan and P. Fedotova and
               D. Grankin and E. Kupryashin and A. Misailidi and D. Nerus and
               A. Nutalapati and G. Sidorov and I. Efremov and M. Gerasyov and
               D. Pikurov and Y. Senchenko and S. Davidenko and D. Kulikov and
               M. Sultankin and K. Askarbek and O. Shamanin and D. Statovoy and
               E. Zalyaev and I. Zorin and A. Letkin and E. Rusakov and
               A. Silchenko and V. Vorobyov and S. Sobolnikov and A. Postnikov},
    year    = {2026},
    eprint  = {2602.00919},
    archivePrefix = {arXiv},
    primaryClass  = {cs.RO},
    url     = {https://arxiv.org/abs/2602.00919},
}
```

<div align="center">

&copy; 2026 Sber Robotics Center &middot; Manipulation Team

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