💡 The paper presents MolmoAct2, an open action reasoning model for robotics that improves upon previous systems in several ways. Current vision-language-action models aim to provide a single generalist controller for robots, but they have limitations, such as being closed, requiring expensive hardware, or having high latency. MolmoAct2 addresses these issues by introducing several new components, including a specialized vision-language-model backbone called MolmoER, which is trained on a large corpus of data and is designed for spatial and embodied reasoning. The model also includes three new datasets, including the largest open bimanual dataset to date, and an open-weight action tokenizer called OpenFAST. The architecture of the model has been redesigned to include a continuous-action expert and an adaptive-depth reasoning variant called MolmoThink, which reduces latency by only re-predicting depth tokens for scene regions that change between timesteps. The results of the paper show that MolmoAct2 outperforms strong baselines in several simulation and real-world benchmarks, and the model weights, training code, and training data are released for use by others. Overall, MolmoAct2 is a fully open action reasoning model that is designed for practical deployment and advances the state of the art in robotics.