Do We Truly Need So Many Samples? Multi-LLM Repeated Sampling Efficiently Scales Test-Time Compute

This paper presents a simple, effective, and cost-efficient strategy, named ModelSwitch, to improve LLM performance by scaling test-time compute. ModelSwitch builds upon the repeated-sampling-then-voting framework, with a novel twist: incorporating multiple models, even weaker ones, to leverage their complementary strengths that potentially arise from diverse training data and paradigms. By using sample consistency as a signal, our strategy dynamically switches between models. Theoretical analysis highlights the efficiency and performance advantages of our strategy. Extensive experiments on seven datasets demonstrate that our strategy not only outperforms self-consistency and state-of-the-art multi-agent debate approaches, but also significantly reduces inference costs. Additionally, our strategy requires only a few comparable LLMs to achieve optimal performance and can be extended with verification methods, demonstrating the potential of leveraging multiple LLMs in the generation-verification paradigm.

Authors

• Jianhao Chen Nanjing University
• Zishuo Xun Shanghai Artificial Intelligence Laboratory University of Auckland
• Bocheng Zhou Shanghai Artificial Intelligence Laboratory
• Han Qi Shanghai Artificial Intelligence Laboratory
• Hangfan Zhang
• Qiaosheng Zhang Shanghai Artificial Intelligence Laboratory
• Yang Chen Shanghai Artificial Intelligence Laboratory
• Wei Hu State Key Laboratory for Novel Software Technology, Nanjing University, China National Institute of Healthcare Data Science, Nanjing University, China
• Yuzhong Qu Nanjing University
• Shuyue Hu Shanghai Artificial Intelligence Laboratory

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