Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization
LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offers a promising alignment strategy, it is hindered by unreliable semantic rewards derived from sparse test cases or restrictive reference translations. We argue that a robust semantic reward for code translation must be derived directly from the source code. In this paper, we propose CTO to improve code translation with
Record details
Published: 13 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (13 May 2026), “Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization,” evidence record 4399, https://ethics.ai/record/4399 (originally published by arXiv).
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