Sherry Yang - Learning World Models and Agents for High-Cost Environments

Talk Title: Learning World Models and Agents for High-Cost Environments

Abstract: While neural networks have achieved superhuman performance in domains with low-cost simulations—from AlphaGo to LLMs—their application to the physical world is bottlenecked by a fundamental challenge: high-cost interactions. In fields like robotics, ML engineering, and the natural sciences, every action or experiment is expensive and time-consuming. This talk outlines strategies for building intelligent agents that learn efficiently despite these real-world constraints. We first address the physical world by showing how learned world models can serve as high-fidelity simulators for robotics, enabling extensive policy refinement before deployment on costly hardware. We then turn to complex engineering domains, where actions like running an ML program incur significant time delays, and discuss adaptations to reinforcement learning to make it robust for these long action settings. Finally, we show how compositional generative models can navigate the vast hypothesis spaces in science, intelligently proposing experiments to accelerate the pace of discovery.

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