Evolving Deeper LLM Thinking
Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, Dale Schuurmans, Xinyun Chen
2025-01-20
Summary
This paper talks about a new way to make AI language models think more deeply and solve complex problems better. The researchers created a method called Mind Evolution that uses evolution-inspired techniques to improve how AI models come up with solutions.
What's the problem?
Current AI language models are really good at many tasks, but they sometimes struggle with complex problems that require deep thinking or planning. It's like asking a smart student to solve a tricky puzzle - they might get close, but often miss the best solution. Also, many existing methods for improving AI thinking require the problem to be translated into a very specific format, which can be difficult for real-world issues.
What's the solution?
The researchers developed Mind Evolution, which works kind of like how evolution improves species over time. It uses the AI to create many possible solutions, then combines and refines the best ones, just like how genetic traits get passed down and mixed in nature. The cool thing is that Mind Evolution doesn't need the problem to be written in a special computer language - it can work with normal human language descriptions. They tested it on tasks like planning travel itineraries and found it worked really well, solving almost all the problems it was given.
Why it matters?
This matters because it could make AI much better at solving real-world problems that don't have clear-cut answers. Imagine having an AI assistant that could help plan complex projects, design innovative solutions, or even assist in scientific research. By making AI think more deeply and creatively, Mind Evolution could lead to breakthroughs in fields like healthcare, engineering, and business strategy. It's a step towards AI that can truly reason and plan like humans do, which could revolutionize how we use AI in our daily lives and in solving global challenges.
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.