RL Debates 6: Thomas “no reward for you” Ringstrom
In our 6th and final RL Debates presentation, Tom introduced a rigorous framework for compositional planning that replaces the standard scalar reward with pr...
Sensorimotor AI Journal Club
Next session: Thursday, January 08, 2026
Topic: The Final Debate/Synthesis (concluding the first round of RL Debate Series)
Presenters: Eli Sennesh, Fritz Sommer, Niels Leadholm, Adam Lowet, Anne Collins, Thomas J. Ringstrom (moderated by Hadi Vafaii)
Recent Posts
The neuron as a direct data-driven controller (DD-DC) by Moore et al., 2024
We explored DD-DC, a provocative normative theory proposing that neurons are not just information processors, but active feedback controllers that steer thei...
RL Debates 5: Anne “not everything is RL” Collins
In our 5th RL Debates presentation, Anne argued that reward-based learning is not always driven by RL computations. Sometimes it’s working memory combined wi...
RL Debates 4: Adam “I literally measured value in the brain” Lowet
In our 4th RL Debates presentation, Adam presented broad topics on RL and the brain, including his distributional RL paper.
RL Debates 3: Niels “you have 1000 brains in your brain” Leadholm
In our 3rd RL Debates presentation, Niels from the Thousand Brains Project shared how their team is reverse-engineering the cortex to build sensorimotor syst...
RL Debates 2: Fritz “learning for the sake of learning” Sommer
In our 2nd RL Debates presentation, Fritz introduced a first-principles, information-theoretic approach to modeling exploration through the maximization of ‘...
RL Debates 1: Eli “abolish the value function” Sennesh
In our 3rd meeting (the 1st in the RL Debate Series), we kicked off a contentious discussion on the role of the value function in RL, and why Eli believes it...
Bayesian Online learning in Non-stationary Environments (BONE) by Duran-Martin et al., 2025
In our sceond meeting, we covered BONE, a unifying framework for online Bayesian learning when the environment is changing.
Bayesian Online Natural Gradient (BONG) by Jones et al., 2024
In our inaugural meeting, we covered BONG, which shows us how to do online variational inference in a principled manner.