Hybrid Modal Decoupled Fusion for Stable Multilingual Code Generation

Multilingual code generation is a difficult task because of unstable convergence, weak semantic alignment, and task imbalance across programming languages. Current instruction fine-tuning methods often do not handle different tasks well, which leads to biased optimization and weak generalization for low-resource languages. To solve these problems, we present MFTCoder++, an improved fine-tuning framework that uses adaptive task scheduling, attention-guided optimization, adversarial regularization, and hybrid fusion of logical and syntactic parts. It changes training focus in real time, keeps gradients and attention consistent, learns task-independent features, and separates semantic logic from language syntax while joining them through a gating process. This design improves stability, semantic alignment, and transfer across languages. It also gives a more reliable and usable solution for multilingual code generation in software engineering practice.