Improving Best-of-N with Budget-Aware Execution for SWE Agents

The study explores the optimization of Software Engineering (SWE) agents through budget-aware execution strategies that adapt to task difficulty, demonstrating the effectiveness of both cascading and parallel execution methods with early stopping to balance cost and speed without compromising quality. By analyzing task difficulty distribution and utilizing self-confidence scores, the research highlights the inefficiency of uniform compute budgets and instead proposes dynamic strategies such as cascading, which sequentially escalates task attempts to minimize costs, and parallel execution, which launches multiple attempts simultaneously to reduce latency. The introduction of Resolve-Now and Resolve-Later classifiers helps predict the success of task completions, allowing for informed early stopping decisions. Despite some trade-offs between cost and latency, the findings underscore the advantage of tailored execution strategies over a one-size-fits-all approach, achieving significant cost savings and speed improvements while maintaining task quality.

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