Sugar-shack with the lab!! 🍁 Saying goodbye to a very long winter, and welcoming sunnier days 🤩🍃

In lifelong learning, acquiring new tasks can cause ML models to forget previously learned knowledge. For this, our lab introduced TAG (Task-based Accumulated Gradients), a general wrapper on top of adaptive gradient optimizers. 📈

This week, as we celebrated International Women’s Right Day for the 115th time on Sunday, the Chandar Lab wanted to pay tribute to all the amazing women doing research👩‍🎓, and to highlight the cutting-edge work they do at our lab everyday...🧵

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!

‘The Markovian Thinker’, developed by our lab, has been accepted at @iclr-conf.bsky.social 

This work achieved long reasoning without the quadratic attention tax by making LLMs reason in chunks with a bounded state, achieving linear compute, constant memory and scaling beyond its training limits! 🔥

New work, just accepted @ICLR: "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. 🧵👇

Excited to share that we have 3 papers accepted at #ICLR2026! 🇧🇷 Our work this year focuses on efficiency and expressivity: deriving theoretical limits for SSMs, achieving linear scaling for reasoning, and modernizing encoder architectures. A summary of our work 👇 🧵

Can LLMs play Hangman? Spoiler alert: Not yet. Check out “LLMs Can’t Play Hangman: On the Necessity of a Private Working Memory for Language Agents”, led by Davide Baldelli, Ali Parviz, AmalZouaq and Sarath Chandar.

Can LLMs become CAD designers? Check out “CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Design”, which is now published in Transactions on Machine Learning Research (TMLR)!