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inf-seminars - Talk Sept 29: Recent Advances in Reward Modeling for Reinforcement Learning

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  • From: Michael Gutmann <michael.gutmann AT ed.ac.uk>
  • To: <seminars AT inf.ed.ac.uk>, Research Staff <research-staff AT inf.ed.ac.uk>, <research-students AT inf.ed.ac.uk>
  • Subject: Talk Sept 29: Recent Advances in Reward Modeling for Reinforcement Learning
  • Date: Tue, 22 Sep 2026 14:58:12 +0100

Dear all,

Masashi Sugiyama (RIKEN/The University of Tokyo) ) will give a RL-related talk on Tue Sept 29, 11am to noon in G.07 (Informatics Forum). Please see below for details.

If you would like to meet him, please claim a slot here:
https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.google.com%2Fspreadsheets%2Fd%2F1l8uO8H-erVZpwFCJV29y-tYWhLeHvYAV3GpHWA6AhLs%2Fedit%3Fgid%3D0%23gid%3D0&data=05%7C02%7C%7C6309d704ca9642e5294608df18b18b64%7C2e9f06b016694589878910a06934dc61%7C0%7C0%7C639256822948159891%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=PCgPGzZHmEDZcmUE8LYsBM%2FfvDsnromjTztgafhR4%2F0%3D&reserved=0

Best wishes

Michael

-------------
Speaker:
Masashi Sugiyama (RIKEN/The University of Tokyo)
https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ms.k.u-tokyo.ac.jp%2Fsugi%2F&data=05%7C02%7C%7C6309d704ca9642e5294608df18b18b64%7C2e9f06b016694589878910a06934dc61%7C0%7C0%7C639256822948186145%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=gqusVrUEAPAhncHPIfbgbIiDZiUfCZNSHuLfT82d7u8%3D&reserved=0

Title:
Recent Advances in Reward Modeling for Reinforcement Learning

Abstract:
Reinforcement learning (RL) has achieved remarkable success in robotics, games, and language model post-training, where reward signals are essential for effective agent training. In this talk, I will present our recent research on advanced reward modeling. This includes robustifying RL through transfer and weakly supervised
learning, coping with interval-based rewards, and exploring diverse reward aggregation frameworks that move beyond the traditional discounted sum.

Bio:
Masashi Sugiyama received his Ph.D. in Computer Science from the Tokyo Institute of Technology, Japan, in 2001. After serving as an assistant and associate professor at the same institute, he became a professor at the University of Tokyo in 2014. Since 2016, he has also served as the director of the RIKEN Center for Advanced Intelligence Project. His research interests include theories and algorithms of machine learning, such as weakly supervised learning, distribution shift adaptation, and reinforcement learning. He was a keynote speaker at ICLR 2023, and is currently an ICML board member and a NeurIPS advisory board member.



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