Evolving Deeper LLM Thinking

• URL: http://arxiv.org/abs/2501.09891v1
• Date: Fri, 17 Jan 2025 00:41:44 GMT
• Title: Evolving Deeper LLM Thinking
• Authors: Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, Dale Schuurmans, Xinyun Chen,
• Abstract summary: The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine candidate responses. Mind Evolution significantly outperforms other inference strategies such as Best-of-N and Sequential Revision in natural language planning tasks.
• Score: 61.61227021098086
• License:
• 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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