Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
Bowen Zuo, Yinglun Zhu·June 15, 2025
Summary
A method using bandit learning for strategic scaling in large language models boosts performance by 15.04% on MATH-500 and 14.40% on LiveCodeBench. Two studies assess LLMs for reasoning tasks, focusing on few-shot fine-tuning and in-context learning. Pimentel et al. (2023) conduct a fair assessment, while Muennighoff et al. (2025) investigate simple test-time scaling. The vLLM model excels in math questions, outperforming others on hard datasets with algorithms like Entropy and UCB.
Introduction
Background
Overview of large language models (LLMs)
Importance of strategic scaling in LLMs
Objective
To explore the use of bandit learning for strategic scaling in LLMs
To demonstrate performance improvements on specific benchmarks
Method
Data Collection
Selection of benchmarks (MATH-500, LiveCodeBench)
Data characteristics and preparation
Data Preprocessing
Preprocessing steps for the benchmarks
Handling of data for bandit learning algorithms
Bandit Learning Application
Explanation of bandit learning principles
Integration of bandit learning for strategic scaling
Implementation details and algorithm selection (Entropy, UCB)
Performance Evaluation
Benchmark Results
Detailed results on MATH-500 and LiveCodeBench
Performance metrics and comparisons
Comparative Analysis
Comparison with other LLM scaling methods
Highlighting the 15.04% and 14.40% improvements
Reasoning Task Studies
Pimentel et al. (2023)
Overview of the study
Fair assessment methodology
Findings and implications
Muennighoff et al. (2025)
Study focus on simple test-time scaling
Methodology and results
Contribution to the field of reasoning tasks
Model Performance
vLLM Model
Description of the vLLM model
Specialization in math questions
Performance on hard datasets
Conclusion
Summary of Findings
Recap of the method's effectiveness
Key performance indicators and benchmarks
Future Directions
Potential areas for further research
Recommendations for practical applications
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How does the bandit learning method strategically scale large language models to achieve performance improvements?
What performance gains were observed on the MATH-500 and LiveCodeBench datasets when using bandit learning for scaling?
Which large language model, specifically vLLM, demonstrates superior performance in solving math questions, and what algorithms contribute to its success?
What are the key differences in the assessment methodologies used by Pimentel et al. (2023) and Muennighoff et al. (2025) when evaluating LLMs for reasoning tasks?