Code Driven Reasoning

Code-driven reasoning explores how large language models (LLMs) can leverage code generation and execution to enhance their reasoning capabilities, particularly for complex tasks beyond simple pattern recognition. Current research focuses on improving LLMs' ability to understand and reason about code, employing techniques like contrastive learning to improve code representations and frameworks like chain-of-thought prompting and code emulation to guide the reasoning process. This approach shows promise for advancing LLMs' performance on tasks requiring logical inference and complex computations, with implications for software engineering, automated program verification, and broader AI applications needing robust reasoning abilities.