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Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL

Paper · 2026 · Zhaofeng Wu, Shiqi Wang, Boya Peng et al.

Summary

LLMs write code well in popular programming languages, but weaker data coverage hurts them in less common ones. This paper asks whether reinforcement learning on one language can improve performance on another without any target-language training data. The authors call this zero-shot cross-programming-language transfer. Using Llama-3.1-8B-Instruct and competition problems from CodeForces, they show that RL in a source language barely helps target languages and can even hurt them. Their explanation is that the SFT starting point matters. They propose Parallel-SFT, which adds synthetically produced, functionally equivalent implementations of the same program in several languages to the SFT mixture. When RL is run on this initialization, both code generation and code validation transfer better to unseen languages. Parallel-SFT beats single-language and ordinary multi-language SFT baselines, and occasionally even beats training directly on the target language. An analysis of hidden states finds that equivalent programs cluster more tightly, especially in middle layers. This suggests a representation organized around functionality, which could help bring long-tail languages up to par cheaply.

Key points

  • Target languages are Go, PHP and Ruby, which rank 15th, 16th and 25th on TIOBE (December 2025). The source-only set adds eight further languages, namely Lua, Bash, JavaScript, C#, Java, C++, C and Python.
  • Baseline experiments ran RL with GRPO-style training on 10 source languages per target. Gains appeared only in-language, and transfer to other languages was flat or negative. Similar behavior appeared when starting from the base model with custom SFT.
  • The authors argue that code, unlike natural language, lacks organic parallel data and structural isomorphism. For comparison, they note that 0.34% of PaLM's pretraining data contains parallel sentences.
  • Unlike translate-train or AST- and IR-based approaches, Parallel-SFT requires no parsers, compilers, or task-specific translations.
  • Llama-3.1 was chosen over Qwen because Qwen models carry a higher risk of benchmark contamination. The authors list as open questions the design choices they did not explore (data format, curricula, mixing ratios) and extensions to agents and reasoning models.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksInformation Systems · 1,730 worksComputer Science Applications · 131 works

🧭 Topics 2Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 882 worksCode & programming languages · 168 works

Cite

Cite as Zhaofeng Wu, Shiqi Wang, Boya Peng, Anuj Kumar Goyal, Melanie Kambadur, Sebastian Ruder, Yoon Kim and Chloe Bi. 2026. Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1324

@inproceedings{wu2026parallel,
  title = {{Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL}},
  author = {Zhaofeng Wu, Shiqi Wang, Boya Peng, Anuj Kumar Goyal, Melanie Kambadur, Sebastian Ruder, Yoon Kim and Chloe Bi},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1324},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1324}
}

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