🚨Scaling RL Most RL methods’ performance saturate at ~5 layers. In this work led by Kevin Wang, we crack the right configuration for scaling Contrastive RL and go beyond 1000 layers NNs! Deep NNs unlock emergent behaviors and other cool properties. Check out Kevin’s thread!

🚀 What happens when you modify the spectrum of singular values of the merged task vector? 🤔 Apparently, you achieve 🚨state-of-the-art🚨 model merging results! 🔥 ✨ Introducing “No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces”

😊 Happy to Share! 🎉 Our paper "Learning Graph Representation of Agent Diffusers (LGR-AD)" has been accepted as a full paper at #AAMAS (A*) International Conference on Autonomous Agents and Multiagent Systems! #diffusion #graphs #agentsystem @ideas-ncbr.bsky.social #WarszawUniversityOfTechnology

Excited to invite you to our #NeurIPS spotlight poster "BRO: Bigger, Regularized, Optimistic"! 🎉 📍 Poster #6302 📅 West Ballroom A-D 🕚 Friday, 11:00-14:00 Join us to discuss with Michał Nauman and me. Let’s talk SOTA in RL! 💪 🧵👇

Self-supervised Learning with Masked Autoencoders (MAE) is known to produce worse image representations than Joint-Embedding approaches (e.g. DINO). In our new paper, we identify new reasons for why that is and point towards solutions: arxiv.org/abs/2412.03215 🧵

My first post here :) Excellent news from NeurIPS. Two papers in, including a spotlight. 1. Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe 2. Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

💠#NeurIPS2024 is coming soon! 📜Let's focus on the BRO algorithm, introduced in 🔦spotlight paper by Michał Nauman, @mateuszostaszewski.bsky.social, Krzysztof Jankowski, @piotrmilos.bsky.social, Marek Cygan, to find out why the second dog runs better ➡️ ideas-ncbr.pl/en/bro-algor...