I am a PhD student at System Intelligence lab in KAIST. I am fortunate to be advised by Professor Jinkyoo Park. During Ph.D, I have closely collaborated with Ling Pan and Yoshua Bengio.

My research interest lies in controllable generative modeling with large models (e.g., LLMs, Diffusion/Flow-based models) via exploring their latent spaces. Especially, I’m interested in building an amortized sampler that can extract crucial latents for generating desired samples. To accomplish this, my research focuses on the amortizing inference of generative models with off-policy RL approaches.

I’m also interested in various decision making problems such as Multi-turn/Multi-agent RL. I’ve also participated in several transportation-related projects based on high-dimensional black-box optimization methods. Here is my cv.

🔥 News

• 2025.10:  🎉🎉 1 paper accepted in 2026 (1 WSDM)
• 2025.06:  🎉🎉 I started internship at Mila, Hosted by Yoshua Bengio
• 2025.05:  🎉🎉 5 papers accepted in 2025 (1 ICLR, 1 CVPR, 2 ICML, 1 KDD)
• 2024.12:  🎉🎉 5 papers accepted in 2024 (1 ICLR, 1 ICML, 1 KDD, 2 NeurIPS)
• 2024.09:  🎉🎉 I started remote internship at HKUST, Hosted by Ling Pan

📖 Educations

• 2024.05 - current, Ph.D in Industrial and Systems Engineering, KAIST (Korea Advanced Institute of Science and Technology)
• 2022.09 - 2024.02, M.S in Graduate School of AI, KAIST
• 2018.03 - 2022.08, B.S in Industrial and Systems Engineering & Computer Science, KAIST

💻 Internships

• 2025.06 - 2025.08, Internship at Mila, hosted by Yoshua Bengio
• 2024.09 - 2025.03, Remote Internship at HKUST (Hong Kong University of Science and Technology), hosted by Ling Pan
• 2021.03 - 2021.08, Internship at Kakao Recommendation Team

📝 Publications

(*: Equal Contribution)

Preprints

• [P4] Improving Sampling Distribution of Off-policy Training in Generative Flow Networks
  [paper] / [code]
  Taeyoung Yun, Sujin Yun, Jinkyoo Park, Ling Pan
  Arxiv 2025

• [P3] Diffusion Alignment as Variational Expectation-Maximization
  [paper] / [code]
  Jaewoo Lee, Minsu Kim, Sanghyeok Choi, Inhyuck Song, Sujin Yun, Hyeongyu Kang, Woocheol Shin, Taeyoung Yun, Kiyoung Om, Jinkyoo Park
  Arxiv 2025

• [P2] Active Attacks: Red-teaming LLMs via Adaptive Environments
  [paper] / [code]
  Taeyoung Yun, Pierre-Luc St-Charles, Jinkyoo Park, Yoshua Bengio, Minsu Kim
  Arxiv 2025

• [P1] Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization
  [paper] / [code]
  Kiyoung Om*, Kyuil Sim*, Taeyoung Yun*, Hyeongyu Kang, Jinkyoo Park
  Arxiv 2025
  NeurIPS 2025 SPIGM Workshop (Oral)

Conference Publications

• [C11] Urban Traffic Network Layout Optimization with Guided Discrete Diffusion Models
  [paper] / [code]
  Taeyoung Yun, Inhyuck Song, Woocheol Shin, Yujin Shin, Sungpil Woo, Sunhwan Lim, Jinkyoo Park
  WSDM 2026

• [C10] Wind Farm Layout Optimization with Diffusion Models
  [paper] / [code]
  Yujin Shin*, Taeyoung Yun*, Sujin Yun, Sungpil Woo, Sunhwan Lim, Jinkyoo Park
  KDD 2025

• [C9] Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
  [paper] / [code]
  Taeyoung Yun*, Kiyoung Om*, Jaewoo Lee, Sujin Yun, Jinkyoo Park
  ICML 2025
  ICLR 2025 FPI Workshop

• [C8] Improved Off-policy Reinforcement Learning in Biological Sequence Design
  [paper] / [code]
  Hyeonah Kim, Minsu Kim, Taeyoung Yun, Sanghyeok Choi, Emmanuel Bengio, Alex Hernández-García, Jinkyoo Park
  ICML 2025
  NeurIPS 2024 AIDrugX Workshop

• [C7] Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation
  [paper] / [code]
  Taeyoung Yun, Dinghuai Zhang, Jinkyoo Park, Ling Pan
  CVPR 2025 Qualcomm Innovation Fellowship Finalist

• [C6] Adaptive Teachers for Amortized Samplers
  [paper] / [code]
  Minsu Kim*, Sanghyeok Choi*, Taeyoung Yun, Emmanuel Bengio, Leo Feng, Jarrid Rector-Brooks, Sungsoo Ahn, Jinkyoo Park, Nikolay Malkin, Yoshua Bengio
  ICLR 2025

• [C5] Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization
  [paper] / [code]
  Taeyoung Yun, Sujin Yun, Jaewoo Lee, Jinkyoo Park
  NeurIPS 2024

• [C4] GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning
  [paper] / [code]
  Jaewoo Lee*, Sujin Yun*, Taeyoung Yun, Jinkyoo Park
  NeurIPS 2024
  ICLR 2024 GenAI4DM Workshop

• [C3] An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems
  [paper] / [code]
  Taeyoung Yun*, Kanghoon Lee*, Sujin Yun, Ilmyung Kim, Won-Woo Jung, Min-Cheol Kwon, Kyujin Choi, Yoohyeon Lee, Jinkyoo Park
  KDD 2024

• [C2] Learning to Scale Logits for Temperature-conditional GFlowNets
  [paper] / [code]
  Minsu Kim*, Juhwan Ko*, Taeyoung Yun*, Dinghuai Zhang, Ling Pan, Woochang Kim, Jinkyoo Park, Yoshua Bengio
  ICML 2024
  NeurIPS 2023 AI4Science Workshop

• [C1] Local Search GFlowNets
  [paper] / [code]
  Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park
  ICLR 2024 (Spotlight)

🎖 Honors and Awards

• 2025.11 Qualcomm Innovation Fellowship Finalist 2025
• 2025.10 Top Reviewer in NeurIPS 2025
• 2021.12 Excellence Award on DACON NH Big Data Competition.
• 2021.09 Dean’s List (Honor for Top 2% Students).