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Most existing chain-of-thought (CoT) prompting methods suffer from the issuesof generalizability and consistency, as they often rely on instance-specificsolutions that may not be applicable to other cases and lack task-levelconsistency in their reasoning steps. To address these limitations, we proposea comprehensive framework, StrategyLLM, harnessing the capabilities of LLMs totackle various tasks. The framework improves generalizability by formulatinggeneral problem-solving strategies and enhances consistency by producingconsistent solutions using these strategies. StrategyLLM employs four LLM-basedagents: strategy generator, executor, optimizer, and evaluator, workingtogether to generate, evaluate, and select promising strategies for a giventask automatically. The experimental results demonstrate that StrategyLLMoutperforms the competitive baseline CoT-SC that requires human-annotatedsolutions on 13 datasets across 4 challenging tasks without human involvement,including math reasoning (39.2% $\rightarrow$ 43.3%), commonsense reasoning(70.3% $\rightarrow$ 72.5%), algorithmic reasoning (51.7% $\rightarrow$ 62.0%),and symbolic reasoning (30.0% $\rightarrow$ 79.2%).

StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving