🚨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!
20.03.2025 23:22 — 👍 1 🔁 1 💬 0 📌 0
🚀 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”
10.02.2025 14:47 — 👍 6 🔁 4 💬 1 📌 0
Reinforcement learning agents should be able to improve upon behaviors seen during training.
In practice, RL agents often struggle to generalize to new long-horizon behaviors.
Our new paper studies *horizon generalization*, the degree to which RL algorithms generalize to reaching distant goals. 1/
04.02.2025 20:37 — 👍 34 🔁 7 💬 1 📌 3
Just hit 100 stars on GitHub with JaxGCRL! ✨
We're working on a frictionless experience so users can discover, install, and run their first experiment in under 10 minutes.
Want to join the team? We're looking for contributors to make JaxGCRL the go-to GCRL repository! 🚀
github.com/MichalBortki...
16.12.2024 19:23 — 👍 3 🔁 0 💬 0 📌 0
For my first post on Bluesky .. I'll start by announcing our 2025 edition of EEML which will be in Sarajevo :) ! I'm really excited about it and hope to see many of you there. Please follow the website (and Bluesky account) for more details which are coming soon ..
15.12.2024 18:39 — 👍 32 🔁 7 💬 1 📌 0
HOT 🔥 fastest, most precise, and most capable hand control setup ever...
Less than $450 and fully open-source 🤯
by @huggingface, @therobotstudio, @NepYope
This tendon-driven technology will disrupt robotics! Retweet to accelerate its democratization 🚀
A thread 🧵
15.12.2024 08:22 — 👍 73 🔁 27 💬 3 📌 2
🤖 Introducing RL Zero 🤖: a new approach to transform language into behavior zero-shot for embodied agents without labeled datasets!
11.12.2024 07:11 — 👍 15 🔁 4 💬 1 📌 2
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! 💪
🧵👇
12.12.2024 15:57 — 👍 3 🔁 1 💬 1 📌 0
Assistant professor at Princeton CS working on reinforcement learning and AI/ML.
Site: https://ben-eysenbach.github.io/
Lab: https://princeton-rl.github.io/
PhD student @Berkeley_AI
reinforcement learning, AI, robotics
Assistant Professor at UW and Staff Research Scientist at Google DeepMind. Social Reinforcement Learning in multi-agent and human-AI interactions. PhD from MIT. Check out https://socialrl.cs.washington.edu/ and https://natashajaques.ai/.
I'm a scientist at Tufts University; my lab studies anatomical and behavioral decision-making at multiple scales of biological, artificial, and hybrid systems. www.drmichaellevin.org
https://Answer.AI & https://fast.ai founding CEO; previous: hon professor @ UQ; leader of masks4all; founding CEO Enlitic; founding president Kaggle; various other stuff…
ML researcher @ Warsaw University of Technology
Reinforcement learning / Neural Networks Plasticity / Neural Network Representations / AI4Science
research scientist @deepmind. language & multi-agent rl & interpretability. phd @BrownUniversity '22 under ellie pavlick (she/her)
https://roma-patel.github.io
ML Research @ Tzafon | Prev: Robot Learning & RL PhD @Technion
More data isn't all we need 🔭🦾 🌍
Member of technical staff @periodiclabs
Open-source/open science advocate
Maintainer of torchrl / tensordict / leanrl
Former MD - Neuroscience PhD
https://github.com/vmoens
Research engineer at InstaDeep working on multi-agent RL
Reward-Free Model-based Maximalist. High-dimensional Empowerment. Self-Preserving Autonomous Agents. Theories of intelligence grounded in compositional control.
RL researcher at DeepMind
https://schaul.site44.com/ 🇱🇺
Professor of Computer Science at Oxford. Senior Staff Research Scientist at Waymo.
RS DeepMind. Works on Unsupervised Environment Design, Problem Specification, Game/Decision Theory, RL, AIS. prev CHAI_Berkeley
Associate prof @ UMass Amherst CICS.
AIignment, reinforcement learning, imitation learning, and robotics.
Researcher in robotics and machine learning (Reinforcement Learning). Maintainer of Stable-Baselines (SB3).
https://araffin.github.io/
AI and Games Researcher at NYU.
Interested in cognition and artificial intelligence. Research Scientist at Google DeepMind. Previously cognitive science at Stanford. Posts are mine.
lampinen.github.io
