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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2605.07248 |
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| _version_ | 1866911661564100608 |
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| author | Yoon, Youngsik Lee, Sungjae Song, Seockbean Wang, Siwei Chen, Wei Ok, Jungseul |
| author_facet | Yoon, Youngsik Lee, Sungjae Song, Seockbean Wang, Siwei Chen, Wei Ok, Jungseul |
| contents | 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\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07248 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PaT: Planning-after-Trial for Efficient Test-Time Code Generation Yoon, Youngsik Lee, Sungjae Song, Seockbean Wang, Siwei Chen, Wei Ok, Jungseul Computation and Language Machine Learning 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\%. |
| title | PaT: Planning-after-Trial for Efficient Test-Time Code Generation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.07248 |