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

We present LS-111B, a 111B-parameter hybrid reasoning model for Korean-English enterprise agents under practical memory and serving con- straints. The model trains from a fully post- trained enterprise language model rather than a new pretraining run, and uses preamble condi- tioning 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 learning with verifiable rewards for multi-step tool-use tasks, language-consistency re- wards for Korean user-facing responses, and 4-bit quantization for single-GPU serving. The adapted model improves mathematical reasoning, func- tion calling, and agentic natural-language-to-SQL (NL2SQL) performance while preserving general Korean and English instruction-following qual- ity. These results provide a practical recipe and failure-mode analysis for adapting post-trained multilingual models to verifiable agentic work- flows under memory-constrained deployment.