Do LLMs Need Inherent Reasoning Before Reinforcement Learning? A Study in Korean Self-Correction

• URL: http://arxiv.org/abs/2601.05459v1
• Date: Fri, 09 Jan 2026 01:17:31 GMT
• Title: Do LLMs Need Inherent Reasoning Before Reinforcement Learning? A Study in Korean Self-Correction
• Authors: Hongjin Kim, Jaewook Lee, Kiyoung Lee, Jong-hun Shin, Soojong Lim, Oh-Woog Kwon,
• Abstract summary: We investigate whether reinforcement learning can enhance Korean reasoning abilities to a degree comparable to English.<n>Our findings reveal that RL alone yields limited improvements when applied to models lacking inherent Korean reasoning capabilities.<n>We show that aligning the model's internal reasoning processes with Korean inputs-particularly by tuning Korean-specific neurons in early layers-is key to unlocking RL's effectiveness.
• Score: 7.756650000650388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) demonstrate strong reasoning and self-correction abilities in high-resource languages like English, but their performance remains limited in low-resource languages such as Korean. In this study, we investigate whether reinforcement learning (RL) can enhance Korean reasoning abilities to a degree comparable to English. Our findings reveal that RL alone yields limited improvements when applied to models lacking inherent Korean reasoning capabilities. To address this, we explore several fine-tuning strategies and show that aligning the model's internal reasoning processes with Korean inputs-particularly by tuning Korean-specific neurons in early layers-is key to unlocking RL's effectiveness. We introduce a self-correction code-switching dataset to facilitate this alignment and observe significant performance gains in both mathematical reasoning and self-correction tasks. Ultimately, we conclude that the crucial factor in multilingual reasoning enhancement is not injecting new linguistic knowledge, but effectively eliciting and aligning existing reasoning capabilities. Our study provides a new perspective on how internal translation and neuron-level tuning contribute to multilingual reasoning alignment in LLMs.

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