@inproceedings{li-etal-2026-codepivot,
title = "{CODEPIVOT}: Bootstrapping Multilingual Transpilation in {LLM}s via Reinforcement Learning with Reduced Reliance on Parallel Corpora",
author = "LI, Shangyu and
Jiang, Juyong and
Ren, Meibo and
Zhong, Sizhe and
Tan, Huiri and
Gou, Yunhao and
Han, Xu and
Chong, Chun Yong and
Peng, Yun and
Shen, Jiasi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1640/",
pages = "34889--34909",
ISBN = "979-8-89176-462-0",
abstract = "Transpilation, or code translation, aims to convert source code from one programming language (PL) to another. Recent large language model (LLM)-based approaches have demonstrated immense potential for code translation. Training-based methods are important because LLMs currently do not effectively adapt to domain-specific settings that suffer from a lack of knowledge without targeted training. However, existing training-based approaches rely on a pairwise transpilation paradigm, making it impractical to support a diverse range of PLs. Furthermore, these methods shape the reinforcement learning (RL) reward linearly in the test-case pass rate, which underweights the earliest partial progress. To address these limitations, we propose CodePivot, a training framework that leverages Python as an intermediate representation (IR), augmented by a novel RL reward mechanism, Aggressive-Partial-Functional reward, to bootstrap the model{'}s multilingual transpilation ability with reduced reliance on parallel corpora. Experiments involving 10 PLs show that the resulting 7B model, trained on Python-to-Others tasks, consistently improves performance across both general and low-resource PL-related transpilation tasks. It outperforms substantially larger mainstream models with hundreds of billions more parameters, such as DeepSeek-R1 and Qwen3-235B-A22B-Instruct-2507, on Python-to-Others tasks and Others-to-All tasks, respectively, and surpasses its counterpart trained directly on Any-to-Any tasks on general transpilation tasks. Code and data: https://github.com/lishangyu-hkust/CodePivot."
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<abstract>Transpilation, or code translation, aims to convert source code from one programming language (PL) to another. Recent large language model (LLM)-based approaches have demonstrated immense potential for code translation. Training-based methods are important because LLMs currently do not effectively adapt to domain-specific settings that suffer from a lack of knowledge without targeted training. However, existing training-based approaches rely on a pairwise transpilation paradigm, making it impractical to support a diverse range of PLs. Furthermore, these methods shape the reinforcement learning (RL) reward linearly in the test-case pass rate, which underweights the earliest partial progress. To address these limitations, we propose CodePivot, a training framework that leverages Python as an intermediate representation (IR), augmented by a novel RL reward mechanism, Aggressive-Partial-Functional reward, to bootstrap the model’s multilingual transpilation ability with reduced reliance on parallel corpora. Experiments involving 10 PLs show that the resulting 7B model, trained on Python-to-Others tasks, consistently improves performance across both general and low-resource PL-related transpilation tasks. It outperforms substantially larger mainstream models with hundreds of billions more parameters, such as DeepSeek-R1 and Qwen3-235B-A22B-Instruct-2507, on Python-to-Others tasks and Others-to-All tasks, respectively, and surpasses its counterpart trained directly on Any-to-Any tasks on general transpilation tasks. Code and data: https://github.com/lishangyu-hkust/CodePivot.</abstract>
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%0 Conference Proceedings
%T CODEPIVOT: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning with Reduced Reliance on Parallel Corpora
%A LI, Shangyu
%A Jiang, Juyong
%A Ren, Meibo
%A Zhong, Sizhe
%A Tan, Huiri
%A Gou, Yunhao
%A Han, Xu
%A Chong, Chun Yong
%A Peng, Yun
%A Shen, Jiasi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-codepivot
%X Transpilation, or code translation, aims to convert source code from one programming language (PL) to another. Recent large language model (LLM)-based approaches have demonstrated immense potential for code translation. Training-based methods are important because LLMs currently do not effectively adapt to domain-specific settings that suffer from a lack of knowledge without targeted training. However, existing training-based approaches rely on a pairwise transpilation paradigm, making it impractical to support a diverse range of PLs. Furthermore, these methods shape the reinforcement learning (RL) reward linearly in the test-case pass rate, which underweights the earliest partial progress. To address these limitations, we propose CodePivot, a training framework that leverages Python as an intermediate representation (IR), augmented by a novel RL reward mechanism, Aggressive-Partial-Functional reward, to bootstrap the model’s multilingual transpilation ability with reduced reliance on parallel corpora. Experiments involving 10 PLs show that the resulting 7B model, trained on Python-to-Others tasks, consistently improves performance across both general and low-resource PL-related transpilation tasks. It outperforms substantially larger mainstream models with hundreds of billions more parameters, such as DeepSeek-R1 and Qwen3-235B-A22B-Instruct-2507, on Python-to-Others tasks and Others-to-All tasks, respectively, and surpasses its counterpart trained directly on Any-to-Any tasks on general transpilation tasks. Code and data: https://github.com/lishangyu-hkust/CodePivot.
%U https://aclanthology.org/2026.findings-emnlp.1640/
%P 34889-34909
Markdown (Informal)
[CODEPIVOT: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning with Reduced Reliance on Parallel Corpora](https://aclanthology.org/2026.findings-emnlp.1640/) (LI et al., Findings 2026)
ACL
- Shangyu LI, Juyong Jiang, Meibo Ren, Sizhe Zhong, Huiri Tan, Yunhao Gou, Xu Han, Chun Yong Chong, Yun Peng, and Jiasi Shen. 2026. CODEPIVOT: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning with Reduced Reliance on Parallel Corpora. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 34889–34909, Budapest, Hungary. Association for Computational Linguistics.