neurovla-cl-libero-goal

NeuroVLA-CL (LIBERO-Goal)

Full-parameter continual-learning checkpoint for the brain-inspired NeuroVLA model, released with the AlphaBrain framework. Provided for direct download and evaluation — no retraining needed.

A NeuroVLA Vision-Language-Action (VLA) model — Qwen2.5-VL backbone feeding a layer-wise Q-Former and a Spiking Neural Network (SNN) action head — fine-tuned sequentially over the 10 LIBERO-Goal tasks with all parameters unfrozen, using Experience Replay (ER, buffer 1000/task) to mitigate catastrophic forgetting. Ships the final task checkpoint only.

Overview

Results

Numbers are conservative estimates over our internal runs; per-run variance is a few percentage points. 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_pytorch_model.pt        full model weights (deploy-ready)

Usage

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

export PRETRAINED_MODELS_DIR=/path/to/models   # must contain Qwen2.5-VL-3B-Instruct/

huggingface-cli download AlphaBrainGroup/neurovla-cl-libero-goal \
    --local-dir ./neurovla_cl

python deployment/model_server/server_policy.py \
    --ckpt_path ./neurovla_cl/task_9_id9_steps_100000_pytorch_model.pt \
    --port 10093 --use_bf16

Reproduction

bash scripts/run_continual_learning_scripts/run_cl_train.sh \
    --yaml configs/continual_learning/neurovla_continual_libero.yaml

License

MIT — see the parent repository.

Citation

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