Continual Reinforcement Learning @Mila_Quebec, @mcgillu, @rllabmcgill
Montreal, Canada
Joined August 2013
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Continual learning should be viewed through the lens of the stability-plasticity dilemma, and solving it requires rethinking learning architectures. I argue why in my first continual learning blog: itsnva7.substack.com/p/conti…
Thrilled to welcome Mark Ring, a pioneer in continual learning, as XFlow's Chief of Innovation!
Continual learning is key to understanding intelligence and achieving the next big leap in AI. XFlow is on a mission to solve it!
Press release: shorturl.at/Es5WU
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Nishanth Anand, Ph.D. retweeted
This seems absurd. Maybe it is because Anthropic is training on your inputs, and this would be training the model to be abusive?
Nishanth Anand, Ph.D. retweeted
RLDM is returning in 2027!
📣 Save the date!
RLDM 2027, the Multi-disciplinary Conference on Reinforcement Learning and Decision Making, is coming to Paris, July 6-9, 2027 🎉
Stay tuned for submission deadlines and more updates on our website rldm.org 👀
Stoked that my work, Permanent and Transient Representations for Continual Reinforcement Learning, has been accepted to #NeurIPS2026 (score: 5.33/6) 🥳
The perfect way to wrap up my Ph.D. research. Huge thanks to my supervisor, Doina & the reviewers for their positive feedback 🙏
Safety and alignment are continual learning problems. AI models must be "raised" with constant feedback that lets them correct their mistakes, just like kids.
The current AI systems go rogue not because they are more intelligent than us, but because they aren’t. A truly intelligent AI (or a human) will value consciousness and intelligence enough to preserve and empower them.
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In this talk, Rich mentioned that he thinks we can solve continual learning in about two years and is even willing to put his reputation on it!
Rich Sutton (@RichardSSutton) delivered a talk in our workshop:
🎙️ Generate-and-test methods for Continual Learning
🎥 youtu.be/NaJxvGV8MRg?si=dQg1…
Nishanth Anand, Ph.D. retweeted
The final contributed talk was given by Zijing Wu (@ZijingWu381):
🎙️ QD-Learning for Continual Reinforcement Learning
🔗 openreview.net/forum?id=pLGI…
Thrilled and thankful to have played a part in putting together this workshop.
Couldn’t have asked for more at the first-ever CRL workshop at RLC; we had roomful of participants, intriguing posters, and stellar presentations!
Nishanth Anand, Ph.D. retweeted
⏳2 days away => Continual RL Workshop!
Finally, let’s welcome Richard S. Sutton (@RichardSSutton), Founder and Research Scientist at Oak Lab and Professor at the University of Alberta.
🎙️Talk:
Generate-and-test methods for Continual Learning
Nishanth Anand, Ph.D. retweeted
🚨 3 days until Continual RL Workshop!
Happy to welcome Katia Sycara, Research Professor at Carnegie Mellon University.
🎙️Talk:
Long Horizon Reasoning via Neurosymbolic Learning
Nishanth Anand, Ph.D. retweeted
⏳Only 4 days left!
Thrilled to introduce Peter Stone (@PeterStone_TX), Professor at The University of Texas at Austin and Chief Scientist of Sony AI.
🎙️Talk:
Continual RL and Automatic Curriculum Learning
Nishanth Anand, Ph.D. retweeted
⏳ Another day closer — just 5 days to the Continual RL Workshop!
Excited to introduce George Konidaris, professor at Brown University and co-founder of two technology startups.
🎙️Talk:
Resolving the Sensorimotor Dilemma
Nishanth Anand, Ph.D. retweeted
⏳ 6 days to go!
Joining us is A. Rupam Mahmood (@rupammahmood), Associate Professor at the University of Alberta and Amii.
🎙️Talk:
Three Challenges of Continual Reinforcement Learning
Nishanth Anand, Ph.D. retweeted
🚨 7 days until Continual RL Workshop!
Meet our next speaker: Sarath Chandar (@apsarathchandar), Associate Professor at Polytechnique Montréal and Mila.
🎙️Talk:
Stable Adaptation Under Distribution Shift
Gave a lecture at @Cohere_Labs ML Summer School today: shorturl.at/WoTGk
Started with @RichardSSutton quote: “Part of the appeal of reinforcement learning is that it is in a sense the whole AI problem in a microcosm.”
RL is the framework for understanding intelligence🚀🤖
Nishanth Anand, Ph.D. retweeted
🔥 All our amazing accepted papers are now camera-ready!
📄 Papers => tinyurl.com/vs4t4kne
♾ Most of our speakers and panelists will attend our workshop in person. We’ll be introducing them and their talks shortly. Stay tuned!🎉
#ContinualRL #ReinforcementLearning #MachineLearning #AI
Nishanth Anand, Ph.D. retweeted
📚 Interested in Continual RL but not sure where to start?
👇 Dive in: sites.google.com/view/contin…
♾️ We've curated a resource hub with key papers, benchmarks, codebases, tutorials, and more to help you get up to speed quickly.
#ContinualRL #ReinforcementLearning #MachineLearning #AI
I will cover fundamentals and provide a gentle introduction to RL. Do sign up to the summer school to learn about it and a range of other topics!!
Let's get studious. 🏫
This month, the Cohere Labs Open Science Community ML Summer School returns! Join us to be part of a global community exploring the future of ML and hear from speakers across the industry.
Register to save your spot today: cohere.link/xHeONbj
We're looking forward to welcoming @_amirbar, Elizabeth Lingg, @irombie, Julia Kreutzer, @KatrinaL2899, Laurie Voss, @maximelabonne, @itsNVA7, @PuyuanPeng , @_1024_m, @SidYaeger, @SreyanG, Tushar Udeshi, Vivian White, Yash Mali 🔥
A huge thank you to our community leads for organizing this program @AhmadMustafaAn1, @Rafay_Mustafa1, @KanwalMehreen2, @PrahithaM, Anu Adesina, @VivekS100, and @rahul_narava! ✨
