Minsu Kim

I am an incoming Senior Researcher at Microsoft Frontier Tuning, where I will work on reinforcement learning (RL) for LLM agents. Before that, I was a postdoctoral fellow working with Prof. Yoshua Bengio at Mila, and Prof. Sungjin Ahn and Prof. Sungsoo Ahn at KAIST.

I received my Ph.D. from KAIST in Prof. Jinkyoo Park’s group, working on RL for combinatorial optimization. Previously, I received my M.S. from KAIST in Prof. Joungho Kim’s group, working on learning-based hardware design optimization. I obtained my B.S. in Mathematics and Computer Science from KAIST.

Research Interests

  • Combinatorial optimization: NP-hard problems and black-box optimization
  • Reinforcement learning: long-horizon credit assignment and sample-efficient exploration
  • AI4Science: molecular sampling/optimization and biological sequence design

Academic Service

  • Area Chair: NeurIPS (Position Paper Track, 2026)
  • Reviewer (Conferences): NeurIPS (2022–2025), ICML (2023–2026), ICLR (2024–2026)
  • Reviewer (Journals): TNNLS (2025–2026), TPAMI (2025), TMLR (2025)

News

May 02, 2026 2 papers (Active Attacks and S3GFN) are accepted at ICML 2026!
Feb 09, 2026 2 papers (LVI and DAV) are accepted at ICLR 2026!
Sep 26, 2025 4 papers (SGDS, TBA, EGM, and ABCD) are accepted at NeurIPS 2025!
May 02, 2025 2 papers (Delta-CS and ODS) are accepted at ICML 2025!
Apr 02, 2025 I’ve selected Jang Yeong SIL Fellowship Award.

Selected Publications

  1. ICML
    Active Attacks: Red-teaming LLMs via Adaptive Environments
    Taeyoung Yun, Pierre-Luc St-Charles, Jinkyoo Park, Yoshua Bengio, and Minsu Kim
    International Conference on Machine Learning, 2026
  2. ICLR
    Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
    Minsu Kim*, Jean-Pierre Falet*, Oliver E Richardson, Xiaoyin Chen, Moksh Jain, Sungjin Ahn, Sungsoo Ahn, and Yoshua Bengio
    International Conference on Learning Representations, 2026
  3. NeurIPS
    On Scalable and Efficient Training of Diffusion Samplers
    Minkyu Kim*, Kiyoung Seong*, Dongyeop Woo, Sungsoo Ahn, and Minsu Kim
    Advances in Neural Information Processing Systems, 2025
  4. Thesis
    Off-policy Training Methods for Probablistic Agents in Combinatorial Space
    Minsu Kim
    Korea Advanced Institute of Science and Technology (KAIST), 2025
  5. AISTATS
    Ant Colony Sampling with GFlowNets for Combinatorial Optimization
    Minsu Kim*, Sanghyeok Choi*, Jiwoo Son, Hyeonah Kim, Jinkyoo Park, and Yoshua Bengio
    International Conference on Artificial Intelligence and Statistics, 2025
  6. ICLR
    Adaptive Teachers for Amortized Samplers
    Minsu Kim*, Sanghyeok Choi*, Taeyoung Yun, Emmanuel Bengio, Leo Feng, Jarrid Rector-Brooks, Sungsoo Ahn, Jinkyoo Park, Nikolay Malkin, and Yoshua Bengio
    International Conference on Learning Representations, 2025
  7. ICML
    Learning to Scale Logits for Temperature-Conditional GFlowNets
    Minsu Kim*, Joohwan Ko*, Taeyoung Yun*, Dinghuai Zhang, Ling Pan, Woochang Kim, Jinkyoo Park, Emmanuel Bengio, and Yoshua Bengio
    International Conference on Machine Learning, 2024
  8. ICLR
    Local Search GFlowNets
    Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, and Jinkyoo Park
    International Conference on Learning Representations, 2024
  9. NeurIPS
    Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
    Minsu Kim, Federico Berto, Sungsoo Ahn, and Jinkyoo Park
    Advances in Neural Information Processing Systems, 2023
  10. NeurIPS
    Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
    Minsu Kim, Junyoung Park, and Jinkyoo Park
    Advances in Neural Information Processing Systems, 2022
  11. NeurIPS
    Learning collaborative policies to solve NP-hard routing problems
    Minsu Kim, Jinkyoo Park, and Joungho Kim
    Advances in Neural Information Processing Systems, 2021