---
license: mit
language:
  - en
library_name: pytorch
pipeline_tag: robotics
tags:
  - robotics
  - continual-learning
  - vla
  - vision-language-action
  - libero
  - lora
  - peft
base_model:
  - Qwen/Qwen2.5-VL-3B-Instruct
datasets:
  - LIBERO
---

# QwenGR00T-CL-LoRA (LIBERO-Goal)

> Continual-learning checkpoint released with the
> [AlphaBrain](https://github.com/AlphaBrainGroup/AlphaBrain) framework.
> Provided for direct download and evaluation — no retraining needed.

A QwenGR00T Vision-Language-Action (VLA) model fine-tuned **sequentially
over the 10 LIBERO-Goal tasks** with Low-Rank Adaptation (LoRA, r=32) on
the VLM backbone and Experience Replay (ER, buffer 1000/task) to mitigate
catastrophic forgetting. Ships the **final task checkpoint** only — the
weights you would use to evaluate the method end-of-stream.

## Overview

| | |
|:---|:---|
| **Architecture**        | QwenGR00T (Qwen2.5-VL-3B + Flow-Matching DiT head) |
| **Base VLM**            | `Qwen/Qwen2.5-VL-3B-Instruct` |
| **Parameters**          | ~3.8 B total · ~82 M trainable (~2.2 %) |
| **LoRA**                | r = 32, α = 16, dropout 0.05, `target_modules: all-linear` |
| **Continual-learning**  | Experience Replay, buffer 1000/task, replay ratio 0.5 |
| **Task stream**         | LIBERO-Goal · 10 tasks · 10 000 steps/task |

## Results

Evaluated with the final checkpoint on all 10 LIBERO-Goal tasks,
10 rollouts per task.

| Metric                                         | Value   |
|:-----------------------------------------------|:-------:|
| Average Success Rate (Avg SR)                  | ~48 %   |
| Negative Backward Transfer (NBT, ↑ better)     | +0.15   |
| Naive sequential fine-tuning baseline (no ER)  | < 10 %  |

> Numbers are conservative estimates over our internal runs; per-run
> variance is a few percentage points depending on seed, simulator state,
> attention implementation, and hardware. Reproduction numbers higher or
> lower than reported are expected — please file an issue / PR with details.

## Files

```
├── README.md                                       model card
├── config.yaml                                     training config (OmegaConf)
├── dataset_statistics.json                         action normalisation (required for inference)
├── task_9_id9_steps_100000_lora_adapter/           LoRA adapter weights + config
└── task_9_id9_steps_100000_action_model.pt         non-VLM weights (DiT head, DINO encoder)
```

## Usage

Clone the [AlphaBrain framework](https://github.com/AlphaBrainGroup/AlphaBrain)
first (the inference server lives there).

```bash
git clone https://github.com/AlphaBrainGroup/AlphaBrain.git
cd VLA-Engine-Developer
pip install -e .

# Set the directory holding the base VLM checkpoint
export PRETRAINED_MODELS_DIR=/path/to/models   # must contain Qwen2.5-VL-3B-Instruct/

# Download this release
huggingface-cli download AlphaBrainGroup/qwengr00t-cl-lora-libero-goal \
    --local-dir ./qwengr00t_cl_lora

# Merge LoRA + non-VLM weights into a deploy-ready .pt
python -m AlphaBrain.training.trainer_utils.peft.merge_lora_checkpoint \
    --base_config      configs/continual_learning/qwengr00t_cl_lora_libero.yaml \
    --lora_adapter_dir ./qwengr00t_cl_lora/task_9_id9_steps_100000_lora_adapter \
    --action_model_pt  ./qwengr00t_cl_lora/task_9_id9_steps_100000_action_model.pt \
    --output_path      ./qwengr00t_cl_lora_final.pt

# Launch the WebSocket inference server
python deployment/model_server/server_policy.py \
    --ckpt_path ./qwengr00t_cl_lora_final.pt --port 10093 --use_bf16
```

For a full 10×10 matrix evaluation against LIBERO-Goal, see the
[CL scripts README](https://github.com/AlphaBrainGroup/AlphaBrain/blob/dev/scripts/run_continual_learning_scripts/README.md).

## Reproduction

```bash
# One command — defaults match this release
bash scripts/run_continual_learning_scripts/run_cl_train.sh
```

The default wrapper loads `configs/continual_learning/qwengr00t_cl_lora_libero.yaml`,
which is exactly the training config shipped here as `config.yaml`.
Expect ~15 h on 2 × NVIDIA A800 80 GB.

## License

MIT — see the [parent repository](https://github.com/AlphaBrainGroup/AlphaBrain).

## Citation

```bibtex
@misc{alphabrain2026,
  title  = {AlphaBrain: A Modular Open-Source Framework for Embodied Intelligence Research},
  author = {AlphaBrain Team},
  year   = {2026},
  url    = {https://github.com/AlphaBrainGroup/AlphaBrain}
}
```