MolmoAct2: Action Reasoning Models for Real-world Deployment
Authors: name: Haoquan Fang, name: Jiafei Duan, name: Donovan Clay, name: Sam Wang, name: Shuo Liu, name: Weikai Huang, name: Xiang Fan, name: Wei-Chuan Tsai, name: Shirui Chen, name: Yi Ru Wang, name: Shanli Xing, name: Jaemin Cho, name: Jae Sung Park, name: Ainaz Eftekhar, name: Peter Sushko, name: Karen Farley, name: Angad Wadhwa, name: Cole Harrison, name: Winson Han, name: Ying-Chun Lee, name: Eli VanderBilt, name: Rose Hendrix, name: Suveen Ellawela, name: Lucas Ngoo, name: Joyce Chai, name: Zhongzheng Ren, name: Ali Farhadi, name: Dieter Fox, name: Ranjay Krishna
Organization: Ai2

Vision-Language-Action (VLA) models aim to provide a single generalist controller for robots, but today's systems fall short on the criteria that matter for real-world deployment. Frontier models are closed, open-weight alternatives are tied to expensive hardware, reasoning-augmented policies pay prohibitive latency for their grounding, and fine-tuned success rates remain below the threshold for dependable use. We present MolmoAct2, a fully open action reasoning model built for practical deployment, advancing its predecessor along five axes. We introduce MolmoER, a VLM backbone specialized for spatial and embodied reasoning, trained on a 3.3M-sample corpus with a specialize-then-rehearse recipe. We release three new datasets spanning low-to-medium cost platforms, including MolmoAct2-BimanualYAM, 720 hours of teleoperated bimanual trajectories that constitute the largest open bimanual dataset to date, together with quality-filtered Franka (DROID) and SO100/101 subsets. We provide OpenFAST, an open-weight, open-data action tokenizer trained on millions of trajectories across five embodiments. We redesign the architecture to graft a flow-matching continuous-action expert onto a discrete-token VLM via per-layer KV-cache conditioning. Finally, we propose MolmoThink, an adaptive-depth reasoning variant that re-predicts depth tokens only for scene regions that change between timesteps, retaining geometric grounding at a fraction of prior latency. In the most extensive empirical study of any open VLA to date, spanning 7 simulation and real-world benchmarks, MolmoAct2 outperforms strong baselines including Pi-05, while MolmoER surpasses GPT-5 and Gemini Robotics ER-1.5 across 13 embodied-reasoning benchmarks. We release model weights, training code, and complete training data. Project page: https://allenai.org/blog/molmoact2