New R3S framework boosts multilingual LLM reasoning without external data

Researchers have developed R3S, a novel reinforcement learning framework designed to improve multilingual understanding and reasoning in large language models. This framework addresses bottlenecks in processing non-English questions by disentangling the optimization of target-language question understanding and reasoning capabilities. R3S refines translation rewards and recovers target-language RLVR signals without requiring external multilingual training data or model feedback. Experiments show significant accuracy improvements on math and general knowledge benchmarks across multiple languages. AI

IMPACT Enhances multilingual capabilities of LLMs, potentially broadening their applicability in non-English contexts.

RANK_REASON The cluster contains an academic paper detailing a new methodology for improving LLM performance. [lever_c_demoted from research: ic=1 ai=1.0]

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• English
• Junxiao Liu
• MMLU-ProX

• paper
• model release

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