title: Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies
    Ahead
summary: Inference-time scaling can enhance the reasoning capabilities of large
  language models (LLMs) on complex problems that benefit from step-by-step
  problem solving. Although lengthening generated scratchpads has proven
  effective for mathematical tasks, the broader impact of this approach on other
  tasks remains less clear. In this work, we investigate the benefits and
  limitations of scaling methods across nine state-of-the-art models and eight
  challenging tasks, including math and STEM reasoning, calendar planning,
  NP-hard problems, navigation, and spatial reasoning. We compare conventional
  models (e.g., GPT-4o) with models fine-tuned for inference-time scaling (e.g.,
  o1) through evaluation protocols that involve repeated model calls, either
  independently or sequentially with feedback. These evaluations approximate
  lower and upper performance bounds and potential for future performance
  improvements for each model, whether through enhanced training or multi-model
  inference systems. Our extensive empirical analysis reveals that the advantages
  of inference-time scaling vary across tasks and diminish as problem complexity
  increases. In addition, simply using more tokens does not necessarily translate
  to higher accuracy in these challenging regimes. Results from multiple
  independent runs with conventional models using perfect verifiers show that,
  for some tasks, these models can achieve performance close to the average
  performance of today's most advanced reasoning models. However, for other
  tasks, a significant performance gap remains, even in very high scaling
  regimes. Encouragingly, all models demonstrate significant gains when inference
  is further scaled with perfect verifiers or strong feedback, suggesting ample
  potential for future improvements.
paper_authors._id: 67ecadb199892db0bda56561
paper_authors.name: Vidhisha Balachandran
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paper_authors.fullname: Vidhisha Balachandran
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paper_authors.name: Jingya Chen
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paper_authors.name: Lingjiao Chen
paper_authors._id: 67ecadb199892db0bda56564
paper_authors.name: Shivam Garg
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paper_authors.name: Neel Joshi
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paper_authors.name: Yash Lara
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paper_authors.fullname: Yash Lara
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paper_authors.user: yashlara
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paper_authors.name: John Langford
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paper_authors.avatarUrl: /avatars/3ecf171bcd5a9eea72127c0c9cea1c6f.svg
paper_authors.fullname: John Langford
paper_authors.type: user
paper_authors.user: johnmech
paper_authors._id: 67ecadb199892db0bda56568
paper_authors.name: Besmira Nushi
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paper_authors.fullname: Besmira Nushi
paper_authors.type: user
paper_authors.user: nushib
paper_authors._id: 67ecadb199892db0bda56569
paper_authors.name: Vibhav Vineet
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/c8a3f5e1e5159ae5ead41bd9fc2b9b34.svg
paper_authors.fullname: Vibhav Vineet
paper_authors.type: user
paper_authors.user: vibhav-vineet
paper_authors._id: 67ecadb199892db0bda5656a
paper_authors.name: Yue Wu
paper_authors._id: 67ecadb199892db0bda5656b
paper_authors.name: Safoora Yousefi
paper_authors.status: admin_assigned
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paper_authors.fullname: Safoora Yousefi
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paper_title: Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies
    Ahead
paper_summary: Inference-time scaling can enhance the reasoning capabilities of large
  language models (LLMs) on complex problems that benefit from step-by-step
  problem solving. Although lengthening generated scratchpads has proven
  effective for mathematical tasks, the broader impact of this approach on other
  tasks remains less clear. In this work, we investigate the benefits and
  limitations of scaling methods across nine state-of-the-art models and eight
  challenging tasks, including math and STEM reasoning, calendar planning,
  NP-hard problems, navigation, and spatial reasoning. We compare conventional
  models (e.g., GPT-4o) with models fine-tuned for inference-time scaling (e.g.,
  o1) through evaluation protocols that involve repeated model calls, either
  independently or sequentially with feedback. These evaluations approximate
  lower and upper performance bounds and potential for future performance
  improvements for each model, whether through enhanced training or multi-model
  inference systems. Our extensive empirical analysis reveals that the advantages
  of inference-time scaling vary across tasks and diminish as problem complexity
  increases. In addition, simply using more tokens does not necessarily translate
  to higher accuracy in these challenging regimes. Results from multiple
  independent runs with conventional models using perfect verifiers show that,
  for some tasks, these models can achieve performance close to the average
  performance of today's most advanced reasoning models. However, for other
  tasks, a significant performance gap remains, even in very high scaling
  regimes. Encouragingly, all models demonstrate significant gains when inference
  is further scaled with perfect verifiers or strong feedback, suggesting ample
  potential for future improvements.
paper_ai_summary: Inference-time scaling improves reasoning capabilities of large language models on complex tasks, showing variable benefits across different problem types and diminishing returns with increased problem complexity.
paper_ai_keywords: large language models LLMs inference-time scaling scratchpads mathematical tasks STEM reasoning calendar planning NP-hard problems navigation spatial reasoning GPT-4o perfect verifiers feedback multi-model inference systems