@inproceedings{yoon-etal-2026-pat,
title = "{P}a{T}: Planning-after-Trial for Efficient Test-Time Code Generation",
author = "Yoon, Youngsik and
Lee, Sungjae and
Song, Seockbean and
Wang, Siwei and
Chen, Wei and
Ok, Jungseul",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1703/",
doi = "10.18653/v1/2026.acl-long.1703",
pages = "36738--36755",
ISBN = "979-8-89176-390-6",
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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<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%.</abstract>
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%0 Conference Proceedings
%T PaT: Planning-after-Trial for Efficient Test-Time Code Generation
%A Yoon, Youngsik
%A Lee, Sungjae
%A Song, Seockbean
%A Wang, Siwei
%A Chen, Wei
%A Ok, Jungseul
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yoon-etal-2026-pat
%X 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%.
%R 10.18653/v1/2026.acl-long.1703
%U https://aclanthology.org/2026.acl-long.1703/
%U https://doi.org/10.18653/v1/2026.acl-long.1703
%P 36738-36755
Markdown (Informal)
[PaT: Planning-after-Trial for Efficient Test-Time Code Generation](https://aclanthology.org/2026.acl-long.1703/) (Yoon et al., ACL 2026)
ACL
- Youngsik Yoon, Sungjae Lee, Seockbean Song, Siwei Wang, Wei Chen, and Jungseul Ok. 2026. PaT: Planning-after-Trial for Efficient Test-Time Code Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 36738–36755, San Diego, California, United States. Association for Computational Linguistics.