Year: 2026
Type: conference-paper
Abstract: Beyond training-time optimization, scaling test-time computation has emerged as a key paradigm to extend the reasoning capabilities of Large Language Models (LLMs).However, most existing methods adopt a rigid Planningbefore-Trial (PbT) policy, which inefficiently allocates test-time compute by incurring planning overhead even on directly solvable problems.We propose Planning-after-Trial (PaT), an adaptive policy for code generation that invokes a planner only upon verification failure.This adaptive policy naturally enables a heterogeneous model configuration: a cost-efficient model handles generation attempts, while a powerful model is reserved for targeted planning interventions.Empirically, across multiple benchmarks and model families, our approach significantly advances the cost-performance Pareto frontier.Notably, our heterogeneous configuration achieves performance comparable to a large homogeneous model while reducing inference cost by approximately 69%.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166861490