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
Published: 2025-12-04 13:46:11

Abstract: 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.