Soichiro Nishimori

About Me

I am Soichiro Nishimori, a PhD student at Sugiyama-Yokoya-Ishida lab supervised by Prof. Sugiyama. Also, I am working as a research part-timer in Imperfect-information Learning Team at RIKEN AIP.

I previously interned at OMRON SINIC X Corporation supervised by Dr. Yoshitaka Ushiku and Dr. Atsushi Hashimoto and Matsuo-Iwasawa lab at The University of Tokyo supervised by Prof. Paavo Parmas.

Mahjong 🀄️ and Tennis 🎾 lover.

Research Interests

I am interested in scalable reinforcement learning (RL) from three perspectives:

• Scale to Data: The amount and quality of data can be traded off. I have worked on offline RL with weak supervision, including domain-unlabeled data RLC2025 and noisy preferences TMLR2026.
• Scale to Computation: I am especially interested in GPU-accelerated RL frameworks, both for simulators, including board games ♟️ Pgx, NeurIPS2023 (Co-authored) and Riichi Mahjong 🀄️ Mahjax, arXiv2026, and for algorithm codebases, such as offline RL JAX-CORL.
• Scale to Trial: Even if an agent can gain experience through massive data or computation, it is not truly scalable unless it can learn efficiently. From this perspective, I have worked on exploration in RL. I focus on a novel exploration objective called ReMax, based on the Retry idea (ICML2026, TMLR2026).