PaT: Planning-after-Trial for Efficient Test-Time Code Generation

• URL: http://arxiv.org/abs/2605.07248v1
• Date: Fri, 08 May 2026 05:09:18 GMT
• Title: PaT: Planning-after-Trial for Efficient Test-Time Code Generation
• Abstract summary: Planning-after-Trial (PaT) is an adaptive policy for code generation that invokes a planner only upon verification failure.<n>We propose PaT, an adaptive policy for code generation that invokes a planner only upon verification failure.
• Score: 25.264318601226265
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Planning-before-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\%.

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