Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning. - Donghu Kim; Hojoon Lee; Kyungmin Lee; Dongyoon Hwang; Jaegul Choo (ICML, 2024)

Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks. - Hojoon Lee; Hyeonseo Cho; Hyunseung Kim; Donghu Kim; Dugki Min; Jaegul Choo; Clare Lyle (ICML, 2024)

Do's and Don'ts: Learning Desirable Skills with Instruction Videos. - Hyunseung Kim; Byungkun Lee; Hojoon Lee; Dongyoon Hwang; Donghu Kim; Jaegul Choo (NeurIPS, 2024)

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning. - Hojoon Lee; Dongyoon Hwang; Donghu Kim; Hyunseung Kim; Jun Jet Tai; Kaushik Subramanian; Peter R. Wurman; Jaegul Choo; Peter Stone 001; Takuma Seno (ICLR, 2025)

Hyperspherical Normalization for Scalable Deep Reinforcement Learning. - Hojoon Lee; Youngdo Lee; Takuma Seno; Donghu Kim; Peter Stone 001; Jaegul Choo (CoRR, 2025)

Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information. - Hojun Cho; Donghu Kim; Soyoung Yang; Chan Lee; Hunjoo Lee; Jaegul Choo (CoRR, 2025)