Vishaal Udandarao

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!

This was work done during my internship with amazing folks @google @deep-mind.bsky.social: @nikparth1.bsky.social (joint-first) Ferjad Talfan @samuelalbanie.bsky.social Federico Yongqin Alessio & @olivierhenaff.bsky.social

Super excited about this direction of strong pretraining for smol models!

Bonus: Along the way, we found current state of CLIP zero-shot benchmarking in disarray—some test datasets have a seed std of ~12%!

We construct a stable & reliable set of evaluations (StableEval) inspired by the inverse-variance-weighting method, to prune out unreliable evals!

Finally, we scale all our insights to pretrain SoTA FLOP-efficient models across three different FLOP-scales: ACED-F{0,1,2}

Outperforming strong baselines including Apple's MobileCLIP, TinyCLIP and @datologyai.com CLIP models!

There's more! ACID and KD are complementary — they can be profitably combined, at scale! Our simple pretraining recipe ACED-ACIDistill showcases continued benefits as we scale to 26B samples seen!

We also show that ACID strongly outperforms KD across different reference/teacher training datasets, KD objectives, and student sizes.

Our ACID method shows very strong scaling properties as the size of the reference model increases, until we hit a saturation point — the optimal reference-student capacity ratio.

Further, ACID significantly outperforms KD as we scale up the reference/teacher sizes.

As our ACID method performs implicit distillation, we can further combine our data curation strategy with an explicit distillation objective, and conduct a series of experiments to determine the optimal combination strategy.

Our online curation method (ACID) uses large pretrained reference models (adopting from prior work: JEST) & we show a theoretical equivalence b/w KD and ACID (appx C in paper).

TLDR: We introduce an online data curation method that when coupled with simple softmax knowledge distillation produces a very effective pretraining recipe yielding SoTA inference-efficient two-tower contrastive VLMs!

🚀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!!
🧵👇

Technical Deep-Dive: Curating Our Way to a State-of-the-Art Text Dataset Our data curation pipeline to obtain substantial improvements in LLM quality, training speed, and inference efficiency.

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...

Great paper! Why do you think it doesn’t make sense for pretraining to be made aware of the model being used in a few-shot setting downstream? Do you see any potential downsides of this kind of approach?

🤔 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:

@bayesiankitten.bsky.social @dziadzio.bsky.social and I also work on continual pretraining :)

Congrats, super exciting!!

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

Tübingen AI Join the conversation

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