Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents

arXiv:2606.31648v1 Announce Type: cross Abstract: We present LuckyStar 111B, a 111B-parameter hybrid reasoning model developed through a collaboration between Cohere and LG CNS for Korean-English enterprise agents under practical memory and serving constraints. The model trains from Cohere's fully post-trained Command A model rather than a new pretraining run, and uses preamble conditioning to switch between concise non-reasoning behavior and longer tool-oriented reasoning. We study four choices for scaling tool-using agents efficiently: multilingual supervised fine-tuning, reinforcement learn

Source: arXiv cs.LG — read the full report at the original publisher.

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