Minsu Kim
Collaboration & Contact
- Actively looking for collaborators → For non-commercial academic research on LLM agents for chip design, please reach out at
minsukim.ai at gmail dot com. I pursue this research independently and would participate in a personal academic capacity, rather than on behalf of my employer. - Professional contact → For seminars, workshops, or other engagements related to my professional role, please reach me at
minsukim at microsoft dot com.
At Microsoft Frontier Tuning, I work on reinforcement learning methods for post-training frontier language models in noisy environments and on long-horizon tasks. Previously, I was a postdoctoral fellow working with Prof. Yoshua Bengio at Mila and Prof. Sungjin Ahn and Prof. Sungsoo Ahn at KAIST, focusing on structured reasoning for trustworthy LLMs.
I received my Ph.D. from KAIST in Prof. Jinkyoo Park’s group, studying reinforcement learning for combinatorial optimization and its applications to LLMs. During my M.S. at KAIST in Prof. Joungho Kim’s group, I studied learning-based physical-layout optimization for semiconductor systems. I received my B.S. in Mathematics and Computer Science from KAIST.
Independent Research Interest
Separately from my professional role, I am independently exploring how LLM agents and reinforcement learning can optimize computing systems and semiconductor physical design—an application area connected to my M.S. research. I am particularly interested in system optimization, floorplanning, routing, and chip placement.
- From code to hardware: The success of coding agents such as Claude Code and Codex shows that LLMs can reason about and optimize complex software, often written in Python. Beneath that software layer are hardware description languages such as Verilog, followed by the physical implementation of circuits. I see these lower layers of the computing stack as a natural next frontier for LLM agents and reinforcement learning.
- Long-term vision: I am interested in a self-improving loop between LLMs for chips and chips for LLMs: agents help design more capable and efficient hardware, and that hardware, in turn, enables more capable models and agents.
Research Themes in Industry
In my professional research, I focus on practical methods that help LLMs tackle reasoning and agentic tasks that current models cannot yet handle reliably. I am particularly interested in settings without readily verifiable rewards—unlike many math and coding tasks—or where success depends on decisions over much longer horizons. The following themes summarize my goals and methods:
- User-Aligned Reasoning Models & Agents: multi-turn reasoning grounded in user intent, multi-agent orchestration, and reward modeling
- Reinforcement Learning at Scale: long-horizon credit assignment, structured exploration, replay-based training, and sample-efficient learning for LLM post-training
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
| Aug 05, 2026 | I joined Microsoft Frontier Tuning as a Senior Research Scientist. |
|---|---|
| Jul 08, 2026 | Our paper, Self-Evolving Curriculum for LLM Reasoning, was accepted to COLM 2026! |
| May 01, 2026 | Two papers—Active Attacks and S3GFN—were accepted to ICML 2026! |
| Feb 08, 2026 | Two papers—LVI and DAV—were accepted to ICLR 2026! |
| Sep 25, 2025 | Four papers—SGDS, TBA, EGM, and ABCD—were accepted to NeurIPS 2025! |