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[2602.16745] PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
Summary
The paper presents PETS, a framework for optimal trajectory allocation aimed at enhancing test-time self-consistency in machine learning models, achieving significant budget efficiency.
Why It Matters
PETS addresses the challenge of improving model performance through efficient trajectory allocation during test-time, a critical aspect in machine learning that can lead to better resource management and enhanced model reliability. This framework provides theoretical foundations and practical algorithms that can be applied in various AI applications.
Key Takeaways
- PETS introduces a new measure, self-consistency rate, for trajectory allocation.
- The framework connects trajectory allocation to crowdsourcing, enhancing theoretical guarantees.
- PETS outperforms uniform allocation, achieving up to 75% budget reduction in offline settings.
- The method adapts to question difficulty in online settings while maintaining efficiency.
- Code availability facilitates further research and application of the framework.
Computer Science > Machine Learning arXiv:2602.16745 (cs) [Submitted on 18 Feb 2026] Title:PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency Authors:Zhangyi Liu, Huaizhi Qu, Xiaowei Yin, He Sun, Yanjun Han, Tianlong Chen, Zhun Deng View a PDF of the paper titled PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency, by Zhangyi Liu and 6 other authors View PDF HTML (experimental) Abstract:Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-time self-consistency under a limited budget remains an open challenge. We introduce PETS (Principled and Efficient Test-TimeSelf-Consistency), which initiates a principled study of trajectory allocation through an optimization framework. Central to our approach is the self-consistency rate, a new measure defined as agreement with the infinite-budget majority vote. This formulation makes sample-efficient test-time allocation theoretically grounded and amenable to rigorous analysis. We study both offline and online settings. In the offline regime, where all questions are known in advance, we connect trajectory allocation to crowdsourcing, a classic and well-developed area, by modeling reasoning traces as workers. This perspective allows us to leverage rich existing theory, yielding theoretical guarantees and an efficient majority-voting-based a...
