This was a fun collaboration between Penn, Princeton, and UT Austin. 🔗 arxiv.org/abs/2605.21214 🧑🎓 @liv-daliberti.bsky.social @cvoelcker.bsky.social @ben-eysenbach.bsky.social @ericeaton.bsky.social
Who's Who of AI
Marcel Hussing
Why they matter
Researcher with public evidence across Agents & robotics, AI research.
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PhD student at the University of Pennsylvania. Prev, intern at MSR, and Meta FAIR. Interested in reliable and replicable reinforcement learning, robotics and knowledge discovery: https://marcelhussing.github.io/
All posts are my own.
What they're sharing
Behavior-Consistent Deep Reinforcement Learning arxiv.org
Oops, I Erred — Collecting, Curating and Using Imperfect Data for Realistic Scenarios workshop.oopsie-data.com
Articles & links
🤖 Coming to CoRL 2026: The first workshop on imperfect robotics data! We’ll explore how to collect, curate & use failures 💢, OOD events❓, and unexpected human behavior 🤔 - data every lab produces but rarely documents, shares, or reuses. Check out workshop.oopsie-data.com
Their own posts
Recent commentary
🚨 New Preprint Alert: Behavior-Consistent Deep Reinforcement Learning 🚨 TLDR: We introduce an approach that achieves behavioral similarity across independent algorithm executions in continuous state-action space deep RL.
At this point I'm not even sure... maybe I prefer LLM reviews.
Like many, I've thought a lot about the developments in theoretical research. And while I'm not as confident as others here on what to make of them, I think one thing is clear. Recent results are yet another historical marker likely left by RL and PG-style methods. #math #openai
I've tried to be a good reviewer, read all the papers carefully, and formed my own view. I'm getting page long, clearly LLM generated rebuttals. It is not my job to prompt your LLM into convincing me. What are people's solutions to this? Should I message the AC? #NeurIPS2026
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