RL researchers, what are you most excited to see next?

Curious what problems people think are closest to a real breakthrough vs. still years out.

A few things I’m watching:

Sample efficiency: model-free methods still need an almost embarrassing amount of environment interaction for tasks humans learn in minutes. Closing that gap feels foundational.

Continual learning: most agents still catastrophically forget when the environment shifts. Getting RL systems that actually accumulate knowledge over time without collapsing feels like a prerequisite for anything deployed in the real world.

Sim-to-real transfer: the gap keeps narrowing but it’s still the bottleneck for most robotics work. Curious if people think domain randomization is a dead end or just undersolved.

RL + world models: Dreamer-style approaches were exciting but haven’t fully delivered on the promise yet. Still feels like there’s a lot left on the table.

Personally most excited about offline RL maturing to the point where it’s practically useful without needing careful dataset curation.

What’s on your radar? And what do you think is overhyped right now?