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Training with more data = better LLMs, right? 🚨 False! Scaling language models by adding more pre-training data can decrease your performance after post-training! Introducing "catastrophic overtraining." 🥁🧵👇 arxiv.org/abs/2503.19206 1/10

1.5 yrs ago, we set out to answer a seemingly simple question: what are we *actually* getting out of RL in fine-tuning? I'm thrilled to share a pearl we found on the deepest dive of my PhD: the value of RL in RLHF seems to come from *generation-verification gaps*. Get ready to 🤿:

super happy about this preprint! we can *finally* perform efficient exploration and find near-optimal stationary policies in infinite-horizon linear MDPs, and even use it for imitation learning :) working with @neu-rips.bsky.social and @lviano.bsky.social on this was so much fun!!

What are the minimal supervised learning primitives required to perform RL efficiently? New paper led by my amazing intern Dhruv Rohatgi: Necessary and Sufficient Oracles: Toward a Computational Taxonomy for Reinforcement Learning arxiv.org/abs/2502.08632 1/

Models can self-improve🥷 by knowing they were wrong🧘‍♀️ but when can they do it? Across LLM families, tasks and mechanisms This ability scales with pretraining, prefers CoT, non QA tasks and more in 🧵 alphaxiv.org/abs/2412.02674 @yus167.bsky.social @shamkakade.bsky.social 📈🤖 #NLP #ML

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models | alphaXiv

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I will present two papers at #NeurIPS2024! Happy to meet old and new friends and talk about all aspects of RL: data, environment structure, and reward! 😀 In Wed 11am-2pm poster session I will present HyPO-- best of both worlds of offline and online RLHF: neurips.cc/virtual/2024...

LLM self-improvement has critical implications in synthetic data, post-training and test-time inference. To understand LLMs' true capability of self-improvement, we perform large-scale experiments with multiple families of LLMs, tasks and mechanisms. Here is what we found: (1/9)

Yuda Song, Hanlin Zhang, Carson Eisenach, Sham Kakade, Dean Foster, Udaya Ghai Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models https://arxiv.org/abs/2412.02674

I think the main difference in terms of interpolation / extrapolation between DPO and RLHF is that the former only guarantees closeness to the reference policy on the training data, while RLHF usually tacks on an on-policy KL penalty. We explored this point in arxiv.org/abs/2406.01462.

(1/n) 💡How can we speed up the serial runtime of long pre-training runs? Enter Critical Batch Size (CBS): the tipping point where the gains of data parallelism balance with diminishing efficiency. Doubling batch size halves the optimization steps—until we hit CBS, beyond which returns diminish.

I created a starter pack for people who are or have been affiliated with the Machine Learning Department at CMU. Let me know if I missed someone! go.bsky.app/QLTVEph #AcademicSky

Ojash Neopane, Aaditya Ramdas, Aarti Singh Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect https://arxiv.org/abs/2411.14341

Intro 🦋 I am a final-year PhD student from CMU Robotics. I work on humanoid control, perception, and behavior in both simulation and real life, using mostly RL: 🏃🏻PHC: zhengyiluo.com/PHC 💫PULSE: zhengyiluo.com/PULSE 🔩Omnigrasp: zhengyiluo.com/Omnigrasp 🤖OmniH2O: omni.human2humanoid.com

Hi Bsky people 👋 I'm a PhD candidate in Machine Learning at Carnegie Mellon University. My research focuses on interactive AI, involving: 🤖 reinforcement learning, 🧠 foundation models, and 👩‍💻 human-centered AI. Also a founding co-organizer of the MineRL competitions 🖤 Follow me for ML updates!