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

• URL: http://arxiv.org/abs/2606.31648v1
• Date: Tue, 30 Jun 2026 13:29:16 GMT
• Title: Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents
• Abstract summary: The model trains from Cohere's fully post-trained Command A model rather than a new pretraining run.<n>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 rewards for Korean user-facing responses.
• Score: 2.427155288825091
• License: http://creativecommons.org/licenses/by/4.0/
• 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 learning with verifiable rewards for multi-step tool-use tasks, language-consistency rewards for Korean user-facing responses, and 4-bit quantization for single-GPU serving. The adapted model improves mathematical reasoning, function calling, and agentic natural-language-to-SQL (NL2SQL) performance while preserving general Korean and English instruction-following quality. These results provide a practical recipe and failure-mode analysis for adapting post-trained multilingual models to verifiable agentic workflows under memory-constrained deployment.

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