Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL
Zhaofeng Wu; Shiqi Wang; Boya Peng; Anuj Goyal; Melanie Kambadur; Sebastian Ruder; Yoon Kim; Chloe Bi · 2026 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Modern language models demonstrate impressive coding capabilities in common programming languages (PLs), such as C++ and Python, but their performance in lower-resource PLs is often limited by training data availability. In principle, however, most programming skills are universal across PLs, so the capability acquired in one PL should transfer to others. In this work, we propose the task of zero-shot cross-programming-language transfer for code RL. We find that, for Llama-3.1, RL training for code generation in a source PL fails to improve, and sometimes even degrades, the performance on othe
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Metadata source: arXiv