SIMA 2: A Generalist Embodied Agent for Virtual Worlds
Authors: name: SIMA team, name: Adrian Bolton, name: Alexander Lerchner, name: Alexandra Cordell, name: Alexandre Moufarek, name: Andrew Bolt, name: Andrew Lampinen, name: Anna Mitenkova, name: Arne Olav Hallingstad, name: Bojan Vujatovic, name: Bonnie Li, name: Cong Lu, name: Daan Wierstra, name: Daniel P. Sawyer, name: Daniel Slater, name: David Reichert, name: Davide Vercelli, name: Demis Hassabis, name: Drew A. Hudson, name: Duncan Williams, name: Ed Hirst, name: Fabio Pardo, name: Felix Hill, name: Frederic Besse, name: Hannah Openshaw, name: Harris Chan, name: Hubert Soyer, name: Jane X. Wang, name: Jeff Clune, name: John Agapiou, name: John Reid, name: Joseph Marino, name: Junkyung Kim, name: Karol Gregor, name: Kaustubh Sridhar, name: Kay McKinney, name: Laura Kampis, name: Lei M. Zhang, name: Loic Matthey, name: Luyu Wang, name: Maria Abi Raad, name: Maria Loks-Thompson, name: Martin Engelcke, name: Matija Kecman, name: Matthew Jackson, name: Maxime Gazeau, name: Ollie Purkiss, name: Oscar Knagg, name: Peter Stys, name: Piermaria Mendolicchio, name: Raia Hadsell, name: Rosemary Ke, name: Ryan Faulkner, name: Sarah Chakera, name: Satinder Singh Baveja, name: Shane Legg, name: Sheleem Kashem, name: Tayfun Terzi, name: Thomas Keck, name: Tim Harley, name: Tim Scholtes, name: Tyson Roberts, name: Volodymyr Mnih, name: Yulan Liu, name: Zhengdong Wang, name: Zoubin Ghahramani
Organization: Deepmind

We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a significant step toward active, goal-directed interaction within an embodied environment. Unlike prior work (e.g., SIMA 1) limited to simple language commands, SIMA 2 acts as an interactive partner, capable of reasoning about high-level goals, conversing with the user, and handling complex instructions given through language and images. Across a diverse portfolio of games, SIMA 2 substantially closes the gap with human performance and demonstrates robust generalization to previously unseen environments, all while retaining the base model's core reasoning capabilities. Furthermore, we demonstrate a capacity for open-ended self-improvement: by leveraging Gemini to generate tasks and provide rewards, SIMA 2 can autonomously learn new skills from scratch in a new environment. This work validates a path toward creating versatile and continuously learning agents for both virtual and, eventually, physical worlds.