POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. - Yeong-Dae Kwon; Jinho Choo; Byoungjip Kim; Iljoo Yoon; Youngjune Gwon; Seungjai Min (NeurIPS, 2020)

Matrix encoding networks for neural combinatorial optimization. - Yeong-Dae Kwon; Jinho Choo; Iljoo Yoon; Minah Park; Duwon Park; Youngjune Gwon (NeurIPS, 2021)

SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning. - Byoungjip Kim; Jinho Choo; Yeong-Dae Kwon; Seongho Joe; Seungjai Min; Youngjune Gwon (CoRR, 2021)

Efficient Active Search for Combinatorial Optimization Problems. - André Hottung; Yeong-Dae Kwon; Kevin Tierney (ICLR, 2022)

Simulation-guided Beam Search for Neural Combinatorial Optimization. - Jinho Choo; Yeong-Dae Kwon; Jihoon Kim; Jeongwoo Jae; André Hottung; Kevin Tierney; Youngjune Gwon (NeurIPS, 2022)

Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation. - JoonHo Lee; Jae Oh Woo; Juree Seok; Parisa Hassanzadeh; Wooseok Jang; JuYoun Son; Sima Didari; Baruch Gutow; Heng Hao; Hankyu Moon; Wenjun Hu; Yeong-Dae Kwon; et al. (ICML, 2024)

CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only. - Junhee Cho; Jihoon Kim; Daseul Bae; Jinho Choo; Youngjune Gwon; Yeong-Dae Kwon (CoRR, 2024)

Less for More: Enhancing Preference Learning in Generative Language Models with Automated Self-Curation of Training Corpora. - JoonHo Lee; JuYoun Son; Juree Seok; Wooseok Jang; Yeong-Dae Kwon (CoRR, 2024)

Reinforcement learning to learn quantum states for Heisenberg scaling accuracy. - Jeongwoo Jae; Jeonghoon Hong; Jinho Choo; Yeong-Dae Kwon (CoRR, 2024)

Preference Consistency Matters: Enhancing Preference Learning in Language Models with Automated Self-Curation of Training Corpora. - JoonHo Lee; JuYoun Son; Juree Seok; Wooseok Jang; Yeong-Dae Kwon (NAACL (Long Papers), 2025)