MolmoAct 7B D LIBERO Goal 0812 EU AI Act Compliance Profile

MolmoAct 7B-D LIBERO-Goal

MolmoAct is a fully open-source action reasoning model for robotic manipulation developed by the Allen Institute for AI. MolmoAct is trained on a subset of OXE and MolmoAct Dataset, a dataset with 10k high-quality trajectories of a single-arm Franka robot performing 93 unique manipulation tasks in both home and tabletop environments. It has state-of-the-art performance among vision-language-action models on multiple benchmarks while being fully open-source. You can find all models in the MolmoAct family here. Learn more about MolmoAct in our announcement blog post or the paper.

MolmoAct 7B-D LIBERO-Goal is based on Qwen2.5-7B and uses SigLip2 as the vision backbone, which is initialized using Molmo's pre-training approach. It is first pre-trained on MolmoAct's Pre-training Mixture, then mid-trained on MolmoAct Dataset, and finally post-trained on LIBERO-Long. This model is intended to be used for replicating our results on LIBERO-Goal.

This checkpoint is a preview of the MolmoAct release. All artifacts used in creating MolmoAct (data, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.

Quick links:

• 📂 All Models
• 📂 All Data
• 📄 [Paper](https://arxiv.org/ab

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