Our paper, Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners, won the Outstanding Paper Award on Emerging Topics in Reinforcement Learning this year at RLC! Congrats to 1st author @cmuslima.bsky.social! Paper: sites.google.com/ualberta.ca/...

Propose some socials for RLC! Research topics, affinity groups, niche interests, whatever comes to mind! rl-conference.cc/call_for_soc...

To do non-LLM RLHF research with real, non-author HUMAN data, last I checked there were few datasets available. Synthetic data is usually quite unrealistic, compromising your results (see bradknox.net/human-prefer...). Our dataset (and code) with real humans: dataverse.tdl.org/dataset.xhtm...

Vibecoding apparently requires a magic touch I lack. In two attempts from scratch, Cursor AI + Claude 3.5 goes off the rails constantly and has eventually degenerated into non-functionality. Degeneration #2: Claude is only pretending to run terminal commands and edit my code. 🤦

Calling a reward function dense or sparse is a misnomer, AFAICT. (1/n)

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

Excited to announce the first RLC 2025 keynote speaker, a researcher who needs little introduction, whose textbook we've all read, and who keeps pushing the frontier on RL with human-level sample efficiency

RLHF algorithms assume humans generate preferences according to normative models. We propose a new method for model alignment: influence humans to conform to these assumptions through interface design. Good news: it works! #AI #MachineLearning #RLHF #Alignment (1/n)