MolmoAct2-Pretrain adapts the Molmo2-ER vision-language backbone into a discrete autoregressive robot policy while keeping the Molmo2 token interface. Robot state is represented with discrete state tokens, and future one-second actions are represented with OpenFAST action tokens.

This checkpoint is the pre-trained VLA backbone before the continuous flow-matching action expert is attached. It is intended for further post-training or fine-tuning, not direct continuous-control inference.

Use this checkpoint for further MolmoAct2 training stages. It was converted with add_action_expert=false, so predict_action(...) is intentionally unavailable. Standard Transformers generation can still be used for VLM-style behavior with trust_remote_code=True.

MolmoAct2 generate robot actions from visual observations and language instructions, but their behavior may vary across embodiments, environments, and hardware configurations. Users should carefully validate model outputs before deployment, especially when operating physical robots or other actuated systems. Where possible, actions should be monitored through interpretable intermediate outputs (adaptive depth map), simulation rollouts, action limits, or other safety checks before execution on hardware. The model’s action space should be bounded by the training data, robot controller limits, and task-specific safety constraints, including limits on speed, workspace, torque, and contact force. Users should follow the hardware manufacturer’s safety guidelines, use appropriate emergency-stop mechanisms, and operate the system only in a safely configured environment with human supervision.