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cs.AI2026
Reliable Self-Evolution with Imperfect Proxy Rewards
Kangjun Noh, Soyu Kim, Kyungwoo Song
Large language model (LLM)-based self-evolving search is a promising approach to scientific discovery. However, high-fidelity evaluation of every candidate is prohibitively expensi…
cs.LG2026
Range-GRPO: Policy Optimization via Pairwise Relations among Reward Intervals
Ryunyi Lee, Kangjun Noh, Somin Kim +2
As the use of large language models (LLMs) expands, post-training has become increasingly important for adapting them to downstream tasks. However, obtaining reliable supervision r…
cs.LG2026
Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses
Kangjun Noh, Seongchan Lee, Ilmun Kim +1
Ensuring factuality is essential for the safe use of Large Language Models (LLMs) in high-stakes domains such as medicine and law. Conformal inference provides distribution-free gu…