title: Evolving Deeper LLM Thinking
summary: 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.
paper_authors._id: 678dc332281a0e32feb5fbfe
paper_authors.name: Kuang-Huei Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T13:16:59.574Z
paper_authors._id: 669fbf44200c01b737751dc5
paper_authors.avatarUrl: /avatars/2023c01b2a8cc1625cafcd0b625871dc.svg
paper_authors.fullname: Kuang-Huei Lee
paper_authors.type: user
paper_authors.user: khlee112
paper_authors._id: 678dc332281a0e32feb5fbff
paper_authors.name: Ian Fischer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T13:18:01.647Z
paper_authors._id: 64ef4f9866f36326b3ec5b8c
paper_authors.avatarUrl: /avatars/9eea179e0797f952664f33e1aef21e88.svg
paper_authors.fullname: Ian Fischer
paper_authors.type: user
paper_authors.user: Ianfischer
paper_authors._id: 678dc332281a0e32feb5fc00
paper_authors.name: Yueh-Hua Wu
paper_authors._id: 678dc332281a0e32feb5fc01
paper_authors.name: Dave Marwood
paper_authors._id: 678dc332281a0e32feb5fc02
paper_authors.name: Shumeet Baluja
paper_authors._id: 678dc332281a0e32feb5fc03
paper_authors.name: Dale Schuurmans
paper_authors._id: 678dc332281a0e32feb5fc04
paper_authors.name: Xinyun Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T13:18:37.820Z
paper_authors._id: 64d0268001931c60161e026a
paper_authors.avatarUrl: /avatars/0ea48c47b0270b449fd6b97b495e64a6.svg
paper_authors.fullname: Xinyun Chen
paper_authors.type: user
paper_authors.user: xinyunchen
paper_title: Evolving Deeper LLM Thinking
paper_summary: 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.
paper_ai_keywords: evolutionary search strategy inference time compute Large Language Models Mind Evolution candidate responses solution evaluator natural language planning tasks TravelPlanner Natural Plan benchmarks Gemini 1.5 Pro formal solver