Extending Test-Time Scaling: A 3D Perspective with Context, Batch, and Turn

• URL: http://arxiv.org/abs/2511.15738v2
• Date: Fri, 21 Nov 2025 15:10:10 GMT
• Title: Extending Test-Time Scaling: A 3D Perspective with Context, Batch, and Turn
• Authors: Chao Yu, Qixin Tan, Jiaxuan Gao, Shi Yu, Hong Lu, Xinting Yang, Zelai Xu, Yu Wang, Yi Wu, Eugene Vinitsky,
• Abstract summary: Reasoning reinforcement learning (RL) has recently revealed a new scaling effect: test-time scaling.<n>We revisit test-time enhancement techniques through the lens of scaling effect.<n>We introduce a unified framework of multi-dimensional test-time scaling to extend the capacity of test-time reasoning.
• Score: 17.841520309337998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning reinforcement learning (RL) has recently revealed a new scaling effect: test-time scaling. Thinking models such as R1 and o1 improve their reasoning accuracy at test time as the length of the reasoning context increases. However, compared with training-time scaling, test-time scaling is fundamentally limited by the limited context length of base models, which remains orders of magnitude smaller than the amount of tokens consumed during training. We revisit test-time enhancement techniques through the lens of scaling effect and introduce a unified framework of multi-dimensional test-time scaling to extend the capacity of test-time reasoning. Beyond conventional context-length scaling, we consider two additional dimensions: batch scaling, where accuracy improves with parallel sampling, and turn scaling, where iterative self-refinement enhances reasoning quality. Building on this perspective, we propose 3D test-time scaling, which integrates context, batch, and turn scaling. We show that: (1) each dimension demonstrates a test-time scaling effect, but with a bounded capacity; (2) combining all three dimensions substantially improves the reasoning performance of challenging testbeds, including IOI, IMO, and CPHO, and further benefits from human preference feedback; and (3) the human-in-the-loop framework naturally extends to a more open-ended domain, i.e., embodied learning, which enables the design of humanoid control behaviors.

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