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cs.AI2026
Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR
Yongjin Yang, Jiarui Liu, Yinghui He +3
Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain reasoning suites spanning mathematics, programming, and science.…
cs.AI2024
MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty
Yongjin Yang, Haneul Yoo, Hwaran Lee
Despite the massive advancements in large language models (LLMs), they still suffer from producing plausible but incorrect responses. To improve the reliability of LLMs, recent res…
cs.AI2024
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding
Haneul Yoo, Yongjin Yang, Hwaran Lee
As large language models (LLMs) have advanced rapidly, concerns regarding their safety have become prominent. In this paper, we discover that code-switching in red-teaming queries…