CuratedThoughts: Data Curation for RL Datasets 🚀 Since DeepSeek-R1 introduced reasoning-based RL, datasets like Open-R1 & OpenThoughts emerged for fine-tuning & GRPO. Our deep dive found major flaws — 25% of OpenThoughts needed elimination by data curation. Here's why 👇🧵

Ever wondered why presenting more facts can sometimes *worsen* disagreements, even among rational people? 🤔 It turns out, Bayesian reasoning has some surprising answers - no cognitive biases needed! Let's explore this fascinating paradox quickly ☺️

🎉 Had fun at #NeurIPS2024 Workshop on #AdaptiveFoundationModels! 🚀 Speakers: @rsalakhu.bsky.social @sedielem.bsky.social Kate Saenko, Matthias Bethge / @vishaalurao.bsky.social Minjoon Seo, Bing Liu, Tianqi Chen 🌐Posters: adaptive-foundation-models.org/papers 🎬 neurips.cc/virtual/2024... 🧵Recap!

Our workshop in numbers: 🖇️ 128 Papers 💬 8 Orals 🖋️ 564 Authors ✅ 40 Reviewers 🔊 7 Invited Speakers 👕 100 T-Shirts 🔥 Organizers: Paul Vicol, Mengye Ren, Renjie Liao, Naila Murray, Wei-Chiu Ma, Beidi Chen #NeurIPS2024 #AdaptiveFoundationModels

🚨Looking to test your foundation model on an arbitrary and open-ended set of capabilities, not explicitly captured by static benchmarks? 🚨 Check out ✨ONEBench✨, where we show how sample-level evaluation is the solution. 🔎 arxiv.org/abs/2412.06745

😵‍💫 Continually pretraining large multimodal models to keep them up-to-date all-the-time is tough, covering everything from adapters, merging, meta-scheduling to data design and more! So I'm really happy to present our large-scale study at #NeurIPS2024! Come drop by to talk about all that and more!

🚀New Paper: Active Data Curation Effectively Distills Multimodal Models arxiv.org/abs/2411.18674 Smol models are all the rage these days & knowledge distillation (KD) is key for model compression! We show how data curation can effectively distill to yield SoTA FLOP-efficient {C/Sig}LIPs!! 🧵👇

ICYMI, check out our latest results @datologyai.com on curating data for LLMs. Intervening only on training data, our pipeline can train models faster (7.7x less compute), better (+8.5% performance), and smaller (models half the size outperform by >5%)! www.datologyai.com/post/technic...

🤔 Can you turn your vision-language model from a great zero-shot model into a great-at-any-shot generalist? Turns out you can, and here is how: arxiv.org/abs/2411.15099 Really excited to this work on multimodal pretraining for my first bluesky entry! 🧵 A short and hopefully informative thread:

1/ Introducing ᴏᴘᴇɴꜱᴄʜᴏʟᴀʀ: a retrieval-augmented LM to help scientists synthesize knowledge 📚 @uwnlp.bsky.social & Ai2 With open models & 45M-paper datastores, it outperforms proprietary systems & match human experts. Try out our demo! openscholar.allen.ai

Here's a fledgling starter pack for the AI community in Tübingen. Let me know if you'd like to be added! go.bsky.app/NFbVzrA