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Reinforcement learning has seen significant success in recent years, but its low sample efficiency limits its application in the real world. World models, as a type of environment generation model, offer hope for addressing this issue. They can serve as simulated environments to train reinforcement learning agents with higher sample efficiency. Currently, most world models simulate environment dynamics through discrete latent variable sequences. However, this method of compressing into a compact discrete representation may overlook visual details that are critical for reinforcement learning. Meanwhile, diffusion models have become prominent in the field of image generation.

Norwegian startup 1X Technologies recently announced significant advancements in developing AI-based world models for robots. Simply put, these models act as virtual testing grounds for robots, allowing them to test and improve in various scenarios without the need for real-world testing. 1X believes this is key to solving the 'robotics challenge' — how to reliably evaluate robots trained for multiple tasks in ever-changing environments.

Title: Artificial Intelligence "Pioneer" Fei-Fei Li Founders World Labs, Raises $230 Million! Recently, Stanford University professor and "AI Pioneer" Fei-Fei Li's new startup World Labs successfully raised $230 million, with investors including renowned venture capital firms Andreessen Horowitz, NEA, and Radical Ventures. This funding values World Labs at over $1 billion.