The article discusses algorithmic reasoning in language models, emphasizing the challenge of generating executable code in a single inference call. It introduces the THINK-AND-EXECUTE framework, which consists of two steps: discovering shared task-level logic expressed as pseudocode and tailoring this logic for specific instances. This approach outperforms traditional instance-specific reasoning methods in seven tasks, demonstrating the enhanced capability of language models to recognize and utilize overarching patterns in problem-solving, thereby improving reasoning performance.
Language models struggle to construct executable code on-the-fly for complex algorithmic reasoning tasks; the THINK-AND-EXECUTE framework addresses this by focusing on task-level logic.
THINK-AND-EXECUTE effectively decomposes reasoning into logical task identification and execution, improving performance over traditional instance-specific methods of reasoning that often fail to recognize overarching patterns.
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