Mind's Eye: Grounded Language Model Reasoning through Simulation (2210.05359v1)

Abstract: Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current 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 LLM 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 LLMs 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 LLMs 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.

• The paper presents a novel approach that integrates physics simulation with language models to enhance reasoning over physical scenarios.
• It employs a text-to-code converter and MuJoCo simulations, achieving 27.9% improvement in zero-shot and 46.0% in few-shot settings.
• Findings indicate that simulation grounding enables smaller LMs to perform comparably to larger models, with wide applications in robotics and autonomous systems.

Mind's Eye: Grounded LLM Reasoning through Simulation

The paper "Mind's Eye: Grounded LLM Reasoning through Simulation" presents a novel methodology for enhancing the reasoning capabilities of LMs by grounding them in the physical world through computational simulations. The primary motivation behind this research is the observation that current LMs, trained predominantly on textual data, lack the real-world grounded experience, which can lead to misrepresentation of knowledge and reasoning errors.

Overview and Methodology

The authors propose a paradigm called Mind's Eye, which integrates a physics simulation engine, specifically DeepMind's MuJoCo, with LMs to improve their reasoning capabilities. The process involves transforming a given physics reasoning question into a rendering code using a text-to-code converter, running a simulation to observe the outcomes, and then appending these simulation results as part of the input to the LMs during inference.

Dataset and Experiments

To evaluate the effectiveness of the proposed method, the authors developed a multi-task physics alignment benchmark, consisting of 39 tasks across six scenes that involve basic physics principles like motion, friction, and collision. The benchmarks help assess how current LMs understand and reason over physical laws.

Experiments demonstrate significant improvements in reasoning accuracy, with an average increase of 27.9% in zero-shot and 46.0% in few-shot settings. Notably, smaller LMs armed with the Mind's Eye paradigm achieved performance comparable to much larger LMs, indicating the efficiency of leveraging simulations for grounding reasoning.

Implications and Future Directions

The results suggest that the integration of simulation-based reasoning can serve as a scalable and effective means to enhance LM capabilities without relying on costly fine-tuning or handcrafted prompts. The practical implications of this research extend to domains where grounded reasoning is essential, such as robotics and autonomous systems. Theoretically, this approach opens avenues for building more general-purpose AI systems that can adapt to unfamiliar contexts by grounding their reasoning in real-world experiences.

Future research could explore extending this methodology to other scientific simulations beyond physical reasoning, aiding decision-making processes in diverse fields such as economics or climate science. Additionally, further advancements in simulator accuracy and efficiency would allow for more complex and realistic grounding scenarios, potentially leading to even higher improvements in AI reasoning.

Conclusion

The paper provides a comprehensive examination of the benefits of simulation-augmented grounding in LLM reasoning. By bridging the gap between textual data and real-world experiences, Mind's Eye contributes to the advancement of AI systems capable of more accurate and reliable reasoning, setting a precedent for future developments in the field.