It's Not That Simple. An Analysis of Simple Test-Time Scaling

• URL: http://arxiv.org/abs/2507.14419v1
• Date: Sat, 19 Jul 2025 00:28:10 GMT
• Title: It's Not That Simple. An Analysis of Simple Test-Time Scaling
• Authors: Guojun Wu,
• Abstract summary: Prior work proposed simple test-time scaling, a method for replicating this scaling behavior with models distilled from o1-like models.<n>This paper presents an analysis of simple test-time scaling and finds that the scaling behavior is largely attributed to scaling down by enforcing a maximum length.
• Score: 1.9906814758497542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prior work proposed simple test-time scaling, a method for replicating this scaling behavior with models distilled from o1-like models by manually controlling test-time compute: either scaling down by enforcing a maximum length or scaling up by iteratively appending "Wait" when the model is about to terminate its generation. This paper presents an analysis of simple test-time scaling and finds that the scaling behavior is largely attributed to scaling down by enforcing a maximum length. In contrast, fine-tuning on long CoT data distilled from o1-like models has no significant impact on scaling behavior, and scaling up by appending "Wait" leads to inconsistencies, as the model may oscillate between solutions. A key distinction exists between scaling down by enforcing a maximum length and scaling up test-time compute in o1-like models, such as DeepSeek-R1\@. These models are typically allowed to utilize as much compute as needed, with the only constraint being the model's maximum supported length. By learning to naturally scale up test-time compute during reinforcement learning, o1-like models surpass their peak performance when scaling up. In contrast, simple test-time scaling progressively imposes a lower upper limit on model performance as it scales down. While replicating the test-time scaling behavior of o1 models can be straightforward by scaling down, it is crucial to recognize that the goal of scaling test-time compute is to unlock higher performance -- beyond what the model could originally achieve -- rather than merely reproducing the appearance of scaling behavior.

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