LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance
arXiv:2605.22567v1 Announce Type: new Abstract: Reinforcement learning has proven effective for enhancing multi-step reasoning in large language models (LLMs), yet its benefits have not fully translated to multilingual contexts. Existing methods struggle with a fundamental trade-off: prioritizing input-language consistency severely hampers reasoning quality, while prioritizing reasoning often leads to unintended language drift toward English. We address this challenge with LANG, a novel framework that leverages language-conditioned hints to guide exploration in non-English reasoning tasks. Our
The proliferation of LLMs and their application in diverse global contexts highlights the current limitations of English-centric AI development, necessitating solutions for multilingual reasoning.
This development can significantly expand the global utility and market for advanced AI, particularly in non-English speaking regions, fostering more inclusive and effective AI applications.
Multilingual AI models can now potentially achieve reasoning quality comparable to English-specific models without severe language drift, moving beyond a fundamental trade-off that previously hampered their development.
- · AI developers in non-English speaking countries
- · Multilingual large language model providers
- · Users of AI in diverse linguistic contexts
- · AI-driven global enterprises
- · English-only AI solution providers
- · Monolingual AI research paradigms
Improved performance and broader adoption of AI applications in non-English speaking markets.
Increased demand for curated, high-quality, non-English language datasets and compute resources specific to these languages.
Enhanced AI capabilities for geopolitical analysis, intelligence gathering, and diplomacy across language barriers, potentially shifting power dynamics in information control.
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