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arXiv · 2501.09891

Evolving Deeper LLM Thinking

Kuang-Huei Lee·Ian Fischer·Yueh-Hua Wu·Dave Marwood·Shumeet Baluja·Dale Schuurmans·Xinyun Chen
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Abstract

We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine candidate responses. The proposed approach avoids the need to formalize the underlying inference problem whenever a solution evaluator is available. Controlling for inference cost, we find that Mind Evolution significantly outperforms other inference strategies such as Best-of-N and Sequential Revision in natural language planning tasks. In the TravelPlanner and Natural Plan benchmarks, Mind Evolution solves more than 98% of the problem instances using Gemini 1.5 Pro without the use of a formal solver.

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Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, Dale Schuurmans, Xinyun Chen. 2025-01-17. Evolving Deeper LLM Thinking. https://arxiv.org/abs/2501.09891

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