Reinforcement Learning from Offline Data and Human Feedback

Description

Reinforcement Learning (RL) has seen remarkable progress in recent years, yet many of its most impressive achievements rely on extensive online interaction, curated environments, or simulated data—conditions rarely available in real-world settings. In contrast, real-world decision-making often depends on learning from limited, imperfect, or passively collected data, alongside guidance from human preferences, demonstrations, or corrections.

This workshop brings together researchers and practitioners exploring the frontiers of Offline Reinforcement Learning (Offline RL) and Reinforcement Learning from Human Feedback (RLHF)—two rapidly growing areas that aim to make RL more robust, safe, and deployable in practice.

Poster Session

This workshop will include a poster session for early career researchers (including graduate students). In order to propose a poster, you must first register for the workshop, and then submit a proposal using the form that will become available on this page after you register. The registration form should not be used to propose a poster.

The deadline for proposing is Wednesday, March 18, 2026. If your proposal is accepted, you should plan to attend the event in-person.

In-Person Registration

Seats are limited at the venue, which means that in-person registration may be capped prior to the workshop start date. If capacity is reached, a waitlist will be imposed, which the registration form will reflect. Early registration is strongly encouraged.

All in-person registrants must wait to receive an invitation to attend in-person from IMSI before traveling, which generally begin to be sent out 4-6 weeks in advance.

All registrants (online and in-person) will receive zoom links and are welcome to attend online.

Registration

IMSI is committed to making all of our programs and events inclusive and accessible. Contact [email protected] to request disability-related accommodations.