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Base Vision-Language-Action (VLA) model released with the AlphaBrain framework. Used as the frozen backbone for the RL-Token continual-learning experiments; also works standalone for LIBERO-Goal.
AlphaBrainGroup/alphabrain-rlt-5traj-alltasks-libero-goal.| Architecture | QwenOFT (Qwen2.5-VL-3B + OFT action head) |
| Base VLM | Qwen/Qwen2.5-VL-3B-Instruct |
| Parameters | ~3.8 B (all trainable during supervised fine-tune) |
| Training data | LIBERO-Goal, 10 tasks × 5 demonstrations each |
| Action head | Parallel continuous action decoding |
| Primary use | Frozen backbone for RL-Token fine-tuning (see companion repo) |
├── README.md model card
├── framework_config.yaml AlphaBrain framework configuration
├── dataset_statistics.json action normalisation statistics (required for inference)
├── model.safetensors full VLA weights (~7.8 GB)
└── qwen_pretrained/ tokenizer, preprocessor, and Qwen2.5-VL config1git clone https://github.com/AlphaBrainGroup/AlphaBrain.git
2cd VLA-Engine-Developer
3pip install -e .
4
5export PRETRAINED_MODELS_DIR=/path/to/models # must contain Qwen2.5-VL-3B-Instruct/
6
7huggingface-cli download AlphaBrainGroup/qwenoft-5traj-libero-goal \
8 --local-dir ./qwenoft_5traj
9
10# Launch the WebSocket inference server
11python deployment/model_server/server_policy.py \
12 --ckpt_path ./qwenoft_5traj --port 10093 --use_bf16AlphaBrainGroup/alphabrain-rlt-5traj-alltasks-libero-goal — RL-Token fine-tuned checkpoint that wraps this base VLA and achieves substantially higher success rates with tiny additional parameters.1@misc{alphabrain2026,
2 title = {AlphaBrain: A Modular Open-Source Framework for Embodied Intelligence Research},
3 author = {AlphaBrain Team},
4 year = {2026},
5 url = {https://github.com/AlphaBrainGroup/AlphaBrain}
6}