DINOv2 is the king for self-supervised learning in images 🦖🦕 But how does it work? I've tried to explain how it works but let's expand on it 🧶

(1/5) Super excited to release our new paper on Reinforcement Learning: "Self-Aligned Reward: Towards Effective and Efficient Reasoners"! Preprint: arxiv.org/pdf/2509.05489

I have three amazing PhD / postdocs on academic job market to start in Fall2026: Chi Han: glaciohound.github.io Xiusi Chen: xiusic.github.io Yuji Zhang: celestinezyj.github.io

🚀 Introducing UserRL: a new framework to train agents that truly assist users through proactive interaction, not just chase static benchmarking scores. 📄 Paper: arxiv.org/pdf/2509.19736 💻 Code: github.com/SalesforceAIRe…

🚨 New paper alert at COLM 2025! 🚨 An interesting open problem for those into Sparse Autoencoders (SAEs): "Top-K activation constrains L0 (the number of non-zeros), but how do we obtain E[L0]?" This was even the very first limitation noted in Leo Gao’s recent paper. (1/8)

🚨 New preprint out! 🚨 In "BAP v2: An Enhanced Task Framework for Instruction Following in Minecraft Dialogues", we work towards a core AI challenge: how can agents follow complex, conversational instructions in a dynamic 3D world? To this end, we introduce an enhanced task

Multimodal conversational agents struggle to follow complex policies, which also impose a fixed computational cost. We ask: 👉 How can we achieve stronger policy-following behavior without having to include policies in-context? 🌐: mikewangwzhl.github.io/TriMPI/ 🧵1/3

Generative World Models will inevitably be computationally demanding, potentially scaling beyond even the requirements of today’s LLMs. But we believe they are a crucial research direction to explore in the future of rendering and spatial intelligence. worldlabs.ai/blog/rtfm

How does high-fidelity tactile simulation help robots nail the last millimeter? We’re releasing VT-Refine, accepted to CoRL: a real-to-sim-to-real visuo-tactile policy using a GPU-parallel tactile sim for our piezoresistive skin FlexiTac. Then fine-tuning a diffusion policy with