Nilaksh
29 posts
@nilaksh404
CS PhD student at @Mila_Quebec and ELLIS. Interested in World Models. Previously worked at @EPFL, @MPI_IS
What if AI learned physics the way Newton did – by experiencing it? We built Sim2Reason: train LLMs inside virtual worlds governed by real physics laws, zero human annotation. Result: +5–10% improvement on International Physics Olympiad, zero-shot. 🧵
📢Excited to announce the Workshop on Weight-Space Symmetries @icmlconf! We welcome 4-page submissions analysing symmetries, their effects on training and model structure, and practical methods to utilize them. Submission Deadline: April 24 (23:59 AoE) #ICML2026
Streaming Reinforcement Learning (RL) is a huge challenge: transitions are used once and discarded immediately. This makes agents extremely sample-inefficient. But what if we could "squeeze" more information out of every single frame? Check out our latest paper!
New work from our lab, accepted @iclr_conf : "The Expressive Limits of Diagonal SSMs for State-Tracking" We give a complete characterization of what diagonal SSMs can and cannot compute on state-tracking tasks and the answer is deeply connected to group theory. 🧵👇
