Marcel Hussing

Too many papers sound like this

Hierarchical Context-Aware Diffusion-Transformer Meta-World-Model Reinforcement Learning with Causally Disentangled Preference-Aligned Self-Supervised Compositional Multi-Scale Latent Skill Priors for Long-Horizon Generalist Decision Making

One reason I work on replicable and consistent RL is because it is has always been at the top of the list of criteria for reliability.

Excuse me? Surely telling me that didn't require much thinking.

I have seen multiple times now that a reviewer said sth like: the proofs are simple -> reject the paper. That is completely counter-productive. A theorem needs to generate new insights. If we learn something new from something simple that should be preferred. Don't believe me? Ask someone famous:

Why are you doing this to me

I think it's relatively simple. One side already has the job they want and the other side needs citations to get that job. And everyone tells me advertising work is how you get citations. I have been tempted to go back because on bsky, interactions have become fewer and fewer. Not going to though...

Yet somehow every now and then a paper becomes very popular even though its findings are similar to those of many others. This paper gets cited while the others don't. Was it just luck?

While I agree with the sentiment let me play devil's advocate. You only get invited to give talks if your work is already well known. Conferences have become too large to even find relevant people. And social media posts are only marginally relevant if you don't already have a large following.

I understand that that is okay but at some point it honestly becomes disheartening if you constantly have to reach out to people. There are others who don't seem to have to; what are they doing differently?

I’m trying to understand whether this is mostly about keyword mismatch, venue visibility, social media, etc.
For example, when I search terms like “high update ratio RL” on Scholar, our papers show up near the top.

scholar.google.com/scholar?hl=e...

Where are things going wrong?

I’ve been thinking about a practical question and would love some opinions:

How do your papers actually get discovered/cited?

I was searching for recent work on high update ratio RL and found several very closely related papers tackling the same failure modes we study. None cited our earlier work.

🚀 Excited to share REPPO, a new on-policy RL agent!

TL;DR: Replace PPO with REPPO for fewer hyperparameter headaches and more robust training.

REPPO, led by @cvoelcker.bsky.social, will be presented at ICLR 2026. How does it work? 🧵👇

Scaling Laws in Particle Physics Data! This is a result I've been itching to share and it's finally out. One of the big open questions is how much better AI-based methods at particle colliders can still become. 1/4

Not a particle physics person but extremely curious, can you elaborate what we might hope to learn from these models in the future? What physics might we discover using them?

"Scientific reviewers should have experience publishing scientific work in related areas" is really not that hot of a take.

Clicking like on any relevant ICLR paper. Encourage people to post their work here more!

How do I see this?

The other paper accepted to @iclr-conf.bsky.social 2026 🇧🇷. Our work on replicable RL sheds some light on how to consistently make decisions in RL.

@ericeaton.bsky.social @mkearnsphilly.bsky.social @aaroth.bsky.social @sikatasengupta.bsky.social @optimistsinc.bsky.social

Two papers accepted to @iclr-conf.bsky.social 2026! One of the is REPPO, see below! I think it deserves a lot more recognition. Let's chat about it in Rio! 🇧🇷

Quite disheartening that there isn't a single workshop at ICLR to present my RL work but there several topics that are listed 5 or 6 times just named differently.

That's correct, we did make it bold

Our number went down by 0.01 but it's very expensive to run so we can't have error bars. Our algorithm is so much better than the rest, new SOTA!

I can't believe that this paper is not yet used by literally everyone. Claas doing all he can to make your life easier. Check it out.

Bringing this back up.

Excited about a new paper! Multicalibration turns out to be strictly harder than marginal calibration. We prove tight Omega(T^{2/3}) lower bounds for online multicalibration, separating it from online marginal calibration for which better rates were recently discovered.

For me, it's mostly verbalizing code I already know I want. I don't write whole apps. I take my RL code and add entropy regularization to TD3. Then I verify. Be explicit about what needs to change and know ahead what changes you expect. I still do the thinking, I just worry less about code context.

I like this site a lot but too few people are posting interesting ML content imo and if they do too infrequently. I realize this for myself a lot.

A Multi-Robot Platform for Robotic Triage Combining Onboard Sensing and Foundation Models This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmanned aerial vehicles (...

This project is a huge team effort across @grasplab.bsky.social and Penn trauma led by PIs @ericeaton.bsky.social and CJ Taylor. Shoutout to @jasonahughes.bsky.social, Raj Kannapiran, and Edward Zhang who did a lot of the heavy lifting. Check out the technical report arxiv.org/abs/2512.08754 (5/5)

Then the ML takes over. Onboard models estimate breathing and heart rate from radar and thermal, and read injuries from multi view images and audio. Fine tuned VLMs plus Grounding DINO and SAM2 convert data into a triage report for responders. (4/5)

Our robots do the dangerous first pass. Falcon drones sweep the scene from above using RGB and thermal cameras to detect and geolocate casualties in day or night. Jackal ground robots then drive in for close up sensing and send a live victim map to responders. (3/5)