Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies
  Ahead
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.
67ecadb199892db0bda56561
Vidhisha Balachandran
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2025-04-03T08:28:50.955Z
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Vidhisha Balachandran
user
vidhishab
67ecadb199892db0bda56562
Jingya Chen
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Lingjiao Chen
67ecadb199892db0bda56564
Shivam Garg
67ecadb199892db0bda56565
Neel Joshi
67ecadb199892db0bda56566
Yash Lara
admin_assigned
2025-04-02T12:25:10.416Z
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Yash Lara
user
yashlara
67ecadb199892db0bda56567
John Langford
admin_assigned
2025-04-02T12:25:29.551Z
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John Langford
user
johnmech
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Besmira Nushi
admin_assigned
2025-04-02T12:25:35.619Z
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Besmira Nushi
user
nushib
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Vibhav Vineet
admin_assigned
2025-04-02T12:25:41.418Z
63c8527becdb7c9fdd9cacc6
/avatars/c8a3f5e1e5159ae5ead41bd9fc2b9b34.svg
Vibhav Vineet
user
vibhav-vineet
67ecadb199892db0bda5656a
Yue Wu
67ecadb199892db0bda5656b
Safoora Yousefi
admin_assigned
2025-04-02T12:25:59.919Z
632aadf1a968c34257d9f506
https://cdn-avatars.huggingface.co/v1/production/uploads/1663741551623-632aadf1a968c34257d9f506.jpeg
Safoora Yousefi
user
safooray
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies
  Ahead
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.
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.
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