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Supervised fine-tuning outperforms complex reinforcement learning techniques in ensuring multilingual API reliability, challenging the notion that more sophisticated methods are always necessary.
English-only post-training of LLMs is suboptimal: even a single non-English language improves both English performance and cross-lingual generalization.
