It's about time! The key principles of reinforcement learning (e.g., learning by interaction, TD learning) are fundamental to intelligence.

Some extra motivation for those of you in RLC deadline mode: our line-up of keynote speakers -- as all accepted papers get a talk, they may attend yours! @rl-conference.bsky.social

Olympiad problems are designed to have elegant solutions and new problems are often designed around old patterns. As language models get better we should expect them to conquer IMO/IOI problems. Simple problems not designed to have elegant solutions would prove harder for language models.

How to keep the AI hype cycle going: 1. Propose new knowledge based benchmarks and show existing LLMs do poorly on them. 2. Train LLMs on the knowledge required to do well on the new benchmarks. 3. Use improvements on the benchmarks as signs of rapid progress.

Almost all robotics startups are betting on learning from large supervised learning datasets collected by teleoperation. The odds of success for this strategy are small. Like the Sim2Real bubble, this bubble might not burst for years. At-least it's keeping the roboticists employed.

Despite significant growth of the AI community some promising research directions are untouched because we rely on a homogeneous set of tools (e.g., autograd). Ideas that are easy to implement with existing tools win the software lottery and are more thoroughly tested.

This year's (first-ever) RL conference was a breath of fresh air! And now that it's established, the next edition is likely to be even better: Consider sending your best and most original RL work there, and then join us in Edmonton next summer!

RLC will be held at the Univ. of Alberta, Edmonton, in 2025. I'm happy to say that we now have the conference's website out: rl-conference.cc/index.html Looking forward to seeing you all there! @rl-conference.bsky.social #reinforcementlearning