Nikhil Anand @nikhil_anand91
Physicist-turned-machine-learner, currently research scientist @kempnerinst nikhilanand91.github.io Cambridge, MA Joined December 2019-
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I respectfully disagree with Ed. Was Kepler's planetary analysis "real" mathematics or just astronomy? Are IMO problems "real" mathematics or just puzzles for high school students? Is photography "real" art or just tool use? The label "real" is a personal, aesthetic judgment,…
I respectfully disagree with Ed. Was Kepler's planetary analysis "real" mathematics or just astronomy? Are IMO problems "real" mathematics or just puzzles for high school students? Is photography "real" art or just tool use? The label "real" is a personal, aesthetic judgment,…
Interested in the latest work from the #KempnerInstitute? Check out papers and preprints from June's Research Roundup. kempnerinstitute.harvard.edu/kempner-commun… Abstracts and links below. 🧵 (1/21) #AI #neuroscience #NeuroAI
@isabelpapad @nsaphra @SimmonsEdler @RyanPaulBadman1 @RaymondRChua @johnjvastola @KanakaRajanPhD @elmelis @_valerie_costa_ @Napoolar @EkdeepL @BTolooshams @dunbar_ba @MorrisYau @gershbrain 'Decomposing Elements of Problem Solving: What "Math" Does RL Teach?' Tian Qin, @corefpark, Mujin Kwun, @aaronwalsman, @EranMalach, @nikhil_anand91, @Hidenori8Tanaka , @elmelis doi.org/10.48550/arXiv… (15/21)
New in the #DeeperLearningBlog: Kempner researchers Nikhil Anand (@nikhil_anand91) and Chloe Su (@Huangyu58589918) discuss new work on how numerical precision can impact the accuracy and stability of #LLMs. kempnerinstitute.harvard.edu/research/deepe… #AI (1/2)
Excited to share this work on understanding low-precision instabilities in model training! See our thread below for more details. Paper: arxiv.org/abs/2506.20752 Blogpost: tinyurl.com/lowprecinstabi…
Excited to share this work on understanding low-precision instabilities in model training! See our thread below for more details. Paper: arxiv.org/abs/2506.20752 Blogpost: tinyurl.com/lowprecinstabi…
🚨 New preprint! TL;DR: Backtracking is not the "holy grail" for smarter LLMs. It’s praised for helping models “fix mistakes” and improve reasoning—but is it really the best use of test-time compute? 🤔
How does RL improve performance on math reasoning? Studying RL from pretrained models is hard, as behavior depends on choice of base model. 🚨 In our new work, we train models *from scratch* to study the effect of the data mix on the behavior of RL. arxiv.org/abs/2504.07912
At NeurIPS? Come discuss loss-to-loss prediction and scaling laws with us!
At NeurIPS? Come discuss loss-to-loss prediction and scaling laws with us!
How do different data distributions interact with scaling laws? And how does training data affect test loss? We find simple shifted power law fits can relate performance across (sometimes very disparate) datasets and losses. See David's thread for more details!
How do different data distributions interact with scaling laws? And how does training data affect test loss? We find simple shifted power law fits can relate performance across (sometimes very disparate) datasets and losses. See David's thread for more details!
MoEs increase parameter count but not FLOPs. Do they offer "free lunch", improving performance without paying in compute? Our answer: for memorization, MoEs give performance gains "for free", but have limited benefit for reasoning! Arxiv: arxiv.org/pdf/2410.19034 🦜🦜🦜
Really cool work led by Devin Kwok (McGill/Mila) on making sense of example difficulty. Addresses some key ?s: E.g, How consistent is measured difficulty across inits and for different architectures? Can we fingerprint models using a few key sensitive/hard examples?
Really cool work led by Devin Kwok (McGill/Mila) on making sense of example difficulty. Addresses some key ?s: E.g, How consistent is measured difficulty across inits and for different architectures? Can we fingerprint models using a few key sensitive/hard examples?
Happy to share our EMNLP paper w/ @jtan189 where we apply Variance of Gradients (VoG) – originally developed by @_cagarwal, @mrdanieldsouza, and @sarahookr – for selecting important data in language-based tasks. At EMNLP? Let's connect to discuss data quality and/or LLMs! #EMNLP
Happy to share our EMNLP paper w/ @jtan189 where we apply Variance of Gradients (VoG) – originally developed by @_cagarwal, @mrdanieldsouza, and @sarahookr – for selecting important data in language-based tasks. At EMNLP? Let's connect to discuss data quality and/or LLMs! #EMNLP
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Kempner Institute at ... @KempnerInst
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