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Come checkout our poster on CodeIt today at the #ICLR2024 #AGIWorkshop 4pm Halle A7. We tackle François Chollet’s ARC by self-improving LLMs with prioritized hindsight replay. Blaze(j) Manczak 🇵🇱🇱🇺🇪🇺 Auke Wiggers Corrado Rainone David Zhang Michaël Defferrard Taco Cohen

🚀 Exciting News! 🚀 Join Baharan Mirzasoleiman and me for a 2-hour tutorial on Data-Efficient Learning! Learn the principles behind data curation: the secret sauce powering today’s AI revolution! ⚡️ See you at 1pm on Monday CEST in Hall A8! 🙌 🔗 More details: sjoshi804.github.io/data-efficient…

Hello #GRaM enthusiasts, Sadly our previous twitter account has been compromised, and we won't be able to get hold of it soon. Hence, we have moved to our new handle GRaM Workshop at ICML 2024. Kindly unfollow Gram Workshop and follow us GRaM Workshop at ICML 2024 for more information. ICML Conference

Come see our poster #715 on CodeIt today at #ICML2024 13.30-15.00 Halle C. We approach ARC by self-improving LLMs with prioritized hindsight replay. Blaze(j) Manczak 🇵🇱🇱🇺🇪🇺 Auke Wiggers Corrado Rainone David Zhang Michaël Defferrard Taco Cohen

Had a great time presenting our STAD work at the #ICML2024 SPIGM workshop this weekend. Thanks for the many engaging discussions!

New paper! 💥 Excited to share the project I worked on at Microsoft Research w/ Besmira Nushi 💙💛 & Eric Horvitz! We propose activation steering to enhance LMs’ instruction-following, improving adherence to constraints like format, length, and keyword inclusion🧵 arxiv.org/abs/2410.12877

So you’re seeing 1-2% gains over some benchmarks… but what does this really mean? Where do these gains come from? With “skill-slices”, you can unearth the specific *skills* that a model improves most over another. 🧵on my MSR internship work 🤠

Introducing Latent Program Network (LPN), a new architecture for inductive program synthesis that builds in test-time adaption by learning a latent space that can be used for search 🔎 Inspired by ARC Prize 🧩, we designed LPN to tackle out-of-distribution reasoning tasks!

Thrilled to see our NeurIPS 2024 paper, Sequential Monte Carlo Policy Optimisation (arxiv.org/abs/2402.07963), featured in Kevin's Reinforcement Learning: A Comprehensive Overview, which additionally recognises SMC as a competitive, scalable online planner. A fantastic modern

Mazda Moayeri and finally, openreview.net/pdf?id=Xh6S3X3… by Natasha Butt (and the awesome AI frontiers team at MSR) discusses how to dynamically create benchmarks using multi-LLM agent interaction (at the DATA-FM workshop on 04/28). Vidhisha Balachandran will be presenting again!

In Singapore! We have an exciting set of talks and papers at #ICLR2025. Besmira Nushi 💙💛 , Vibhav and I will be around to chat about our recent inference scaling evaluation report. You can also talk to Mazda Moayeri and Alessandro Stolfo about their internship projects on model understanding.