Title: Training Agentic Models: From Reasoning LLMs to Production SWE Agents

Speaker: Dr. Young Jin Kim @ Microsoft

Time : 11:00 - 12:00, July 15th, 2026

Location: 

Online: https://hli.skku.edu/InvitedTalk260715 

In-person: 31403, Humanities and Social Sciences Campus (Seoul), SKKU

Language: English speech & English slides

Abstract: 

Recent advances in large language models are rapidly shifting AI systems from passive assistants toward autonomous agents capable of reasoning, planning, tool use, and multi-step decision making. In this talk, I will discuss the training of modern agentic models, focusing on the intersection of reasoning language models, reinforcement learning, and software engineering agents.

The talk will cover lessons from building frontier-scale language models and deployed coding agents, including reinforcement learning with verifiable rewards (RLVR), post-training techniques, reasoning-oriented training, and synthetic data generation. I will also discuss several practical challenges in training and deploying agentic systems.

The talk aims to provide both a research perspective and a systems perspective on where agentic AI is heading next.

Bio:

Young Jin Kim is a Member of Technical Staff / Senior Principal Researcher at Microsoft Superintelligence (MSI), where he works on reasoning language models and software engineering agents. His research focuses on reinforcement learning with verifiable rewards (RLVR), agentic AI, Mixture-of-Experts systems, post-training, and large-scale LLM training.

He has contributed to multiple generations of Phi models and production AI systems powering Copilot experiences. He received his Ph.D from Georgia Institute of Technology.