Challenge Track: Divide and Translate: Parameter Isolation with Encoder Freezing for Low-Resource Indic NM

Low-resource tribal languages remain severely underrepresented in modern machine translation, especially when training data is small, noisy, and linguistically diverse. We propose Divide and Translate, a modular framework that replaces unified multilingual fine-tuning with direction-specific expert models. A single frozen NLLB encoder provides a stable multilingual representation space, while independent decoders specialize in individual translation directions. To improve robustness, we apply bitext reversal augmentation, doubling supervision and enabling balanced bi-directional learning without generating synthetic data. This design reduces gradient conflict, mitigates hallucination, and improves generalization under domain shift. Despite minimal compute and limited data, our system achieves strong leaderboard performance with low validation-test divergence, showing that specialization and parameter isolation outperform scale for under-resourced languages.