Evolving Deeper LLM Thinking
FreeEvolutionary search strategy for scaling LLM inference time compute
About Evolving Deeper LLM Thinking
Evolving Deeper LLM Thinking introduces Mind Evolution, an evolutionary search strategy designed to scale inference time compute in Large Language Models. The approach leverages a language model to iteratively generate, recombine, and refine candidate responses, avoiding the need to formalize the underlying inference problem as long as a solution evaluator is available. In natural language planning tasks such as TravelPlanner and Natural Plan benchmarks, Mind Evolution outperforms Best-of-N and Sequential Revision strategies, solving over 98% of problem instances using Gemini 1.5 Pro without a formal solver. The method represents a novel way to enhance LLM reasoning through evolutionary algorithms.
Key Features
Pros & Cons
- Significantly outperforms other inference scaling strategies like Best-of-N and Sequential Revision
- Avoids the complexity of formalizing the problem logic
- Leverages the language model's own generative capabilities for refinement
- High success rate (over 98%) on challenging planning benchmarks
- Requires a problem-specific solution evaluator to guide the search
- May be computationally expensive due to iterative generation and refinement
- Effectiveness is demonstrated mainly on planning tasks; generalizability to other domains needs further study