Extending Test-Time Scaling: A 3D Perspective with Context, Batch, and Turn
Chao Yu, Qixin Tan, Jiaxuan Gao, Shi Yu, Hong Lu, Xinting Yang, Zelai Xu, Yu Wang, Yi Wu, Eugene Vinitsky
TL;DR
This paper introduces a 3D test-time scaling framework for reasoning models, combining context, batch, and turn scaling to significantly enhance reasoning performance on complex tasks.
Contribution
It proposes a unified 3D scaling method that extends test-time reasoning capacity by integrating context, batch, and turn scaling techniques.
Findings
Each scaling dimension shows a test-time scaling effect with bounded capacity.
Combining all three dimensions improves reasoning on challenging benchmarks.
Human feedback and embodied learning further enhance model performance.
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…
Peer Reviews
Decision·Submitted to ICLR 2026
1.Human-in-the-loop Integration: The incorporation of human feedback adds valuable insights into improving model performance beyond traditional scaling approaches. 2.Clear Structure and Readability: The paper is well-organized, and the results are presented in a clear and digestible manner, making it easy to follow the progression of the experiments and conclusions.
1.The paper provides a relatively basic analysis of scaling across different dimensions (multi-turn, batch size), with the conclusion stating: “All three scaling methods achieve substantial improvements at small scales but saturate as the scale becomes larger.” This is a fairly conventional observation and aligns with general expectations, which limits the paper's ability to offer new, groundbreaking insights. 2.the paper does not explicitly address how to allocate resources across these three
- **Clear formalization of three TTS axes (C, B, T):** The paper presents a unified 3D test-time scaling (TTS) procedure with aggregation, including both LLM- and human-judge variants. This provides practitioners with a coherent way to reason about *length* ($C$), *breadth* ($B$), and *depth* ($T$) at inference. - **Empirical breadth across domains:** The evaluation spans mathematics, physics, and code, as well as embodied RL reward design. This cross-domain scope is valuable, even if t
- **Ambiguous compute accounting:** The "total thinking budget" is defined as the theoretical maximum token count, yet 3D methods introduce additional LLM calls (judge, reflection) and expanding prompts across turns. These overheads are not clearly accounted for, making Figure 3 comparisons unreliable and potentially attributing gains to untracked compute. The authors should explicitly include judge tokens and prompt growth on the x-axis and report compute-matched baselines. - **Small, high
1. The paper writing is well-structured with clear motivation. The related work makes it easy for the reader to understand the context. 2. The proposed framework was evaluated in multiple benchmarks in both reasoning tasks and embodied AI tasks. 3. The experimental results provide interesting and useful insights regarding how different dimensions of test-time scaling can extend the capacity of test-time reasoning.
1. The contribution of this work is more like empirical tests about three existing (context, batch, turn) approaches' performance along with the performance when combing the three methods together. There is no fundamental breakthough in model design or algorithm side. The proposed framework seems to be a simple concatenation of existing approaches together. 2. Human evaluation was the key to the experimental results. However, lots of details about human evaluation design were missing. During ev
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Taxonomy
TopicsReinforcement Learning in Robotics · Action Observation and Synchronization · Robot Manipulation and Learning