Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations
While scaling test-time compute can substantially improve model performance, existing approaches either rely on static compute allocation or sample from fixed generation distributions. In this work, we introduce a test-time compute allocation framework that jointly adapts where computation is spent and how generation is performed. Our method begins with a warm-up phase that identifies easy queries and assembles an initial pool of question-response pairs from the test set itself. An adaptive phase then concentrates further computation on unresolved queries while reshaping their generation distributions through evolving in-context demonstrations -- conditioning each generation on successful...
| Authors | Bowen Zuo, Dongruo Zhou, Yinglun Zhu |
|---|---|
| Primary category | cs.AI |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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