Mind's Eye: Grounded Language Model Reasoning through Simulation

TL;DR

Mind's Eye introduces a grounded reasoning paradigm for language models by integrating physics simulations, significantly enhancing their ability to perform physical reasoning tasks and reducing the need for larger models.

Contribution

This work presents a novel grounding approach using physics simulations to improve language model reasoning in physical contexts.

Findings

27.9% zero-shot accuracy improvement

46.0% few-shot accuracy improvement

Smaller models with Mind's Eye match larger models' performance

Abstract

Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world -- their failure to relate language to the physical world causes knowledge to be misrepresented and obvious mistakes in their reasoning. We present Mind's Eye, a paradigm to ground language model reasoning in the physical world. Given a physical reasoning question, we use a computational physics engine (DeepMind's MuJoCo) to simulate the possible outcomes, and then use the simulation results as part of the input, which enables language models to perform reasoning. Experiments on 39 tasks in a physics alignment benchmark demonstrate that Mind's Eye can improve reasoning ability by a large margin (27.9% zero-shot, and 46.0% few-shot absolute accuracy improvement on average). Smaller language models armed with Mind's Eye can obtain similar performance to models that are 100x larger. Finally, we confirm the robustness of Mind's Eye through ablation studies.