SFT Enables Reasoning in Low-Resource Languages for MoE Models

Fine-tuning 3.6-4.0B parameter MoE models for low-resource languages shows that accuracy benchmarks are often too noisy to track progress. However, supervised fine-tuning (SFT) successfully shifts model reasoning traces from English-centric patterns to the target language in 98% of cases.