Finding the Frame

An RLC Workshop for Examining Conceptual Frameworks in RL

August 15, 2026

Montréal, Canada

About

Scientific research is often framed within paradigms of concepts, definitions, axioms, and assumptions — RL is no exception. While such details usually go unchallenged in the pursuit of specific objectives, this workshop brings them into sharp focus. In this workshop, we examine the conceptual foundations of RL and their impact on framing problems. We are excited for this workshop, because we believe it has the potential to open up new territory, test our historical beliefs, and ultimately guide our future research endeavors in productive directions. Overall, we hope to generate enthusiasm and build a community dedicated to examining the conceptual foundations of RL.

This is a full-day workshop. The first half of the day features four short talks, representing different perspectives on RL. Following these talks, these speakers join a panel discussion. Using this panel as backdrop for the rest of the day, we open the floor to submitted work, which includes poster sessions and lightning talks. (See the detailed schedule.)

Call for Papers

Topics. We invite submission of papers that reflect on the philosophy, practice, and formalisms of reinforcement learning. This includes, but is not limited to, the following areas:

Proposals that explore beyond the standard RL problem or paradigm.
Any work rethinking goals, agents, models, values, or other core aspects of the RL paradigm.
Novel benchmarks or applications that explore limitations of our previous paradigms.
Reflections on the basic building blocks or methods of reinforcement learning.
Detailed analysis to expose issues with existing challenges and benchmarks that artificially or deceptively narrow the RL frame.
Work that examines the underlying formulations or assumptions of RL implicit in current solution approaches.
Methods that address new paradigms, where a significant portion of the paper focuses on the novel paradigm. See this paper as an example.
Philosophy of science in RL.
Works that connect RL with large language models and foundation models, including frameworks that challenge or reinterpret core aspects of RL such as reward specification, interaction, state representation, and credit assignment.

We also welcome submissions that interpret our theme in other creative ways. Please see the accepted paper list from Finding the Frame 2025 for examples of how this theme was previously interpreted.

Formatting. There is a page limit of seven content pages (excluding the references, acknowledgements, and appendices). All submissions should be in the RLC format; see the submission guide for more details.

Dual Submissions. Work that is in submission or under review at a conference or journal is welcome. We are not accepting already-published work.