Basavasagar Patil @basavasagar18
@UMRobotics Master's Student. basavasagarkp.github.io Ann Arbor Joined November 2019-
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@jsuarez5341 Making everything one big CUDA graph helped wall clock consistency a lot on a laptop, but we still fought with power management. Looking forward to using an Nvidia Spark in the future. It isn’t in the repo code, but the single biggest win I have seen is an “action input” model…
With some caveats*
Solution to Yann lecun's drifting problem is a system prompt!!
PSA: if you work on plasticity loss you should read "Transient Non-stationarity and Generalisation in Deep Reinforcement Learning" by Igl et al. It's super relevant but suffers from an unfortunate lack of SEO due to predating the "plasticity loss" nomenclature.
ofcourse, they took the spotlight from genie
The progress is crazy on this!!
The progress is crazy on this!!
Lol, I just used Opus to rewrite my torch PPO code to Jax. Let's just say I was nowhere near confident, and I was right, full of compilation errors and bugs
Lol, I just used Opus to rewrite my torch PPO code to Jax. Let's just say I was nowhere near confident, and I was right, full of compilation errors and bugs
Sell my computer
I am excited to announce that our AI institute (Institute for Foundations of Machine Learning, IFML) has been renewed. IFML was part of the first cohort of AI Institutes announced in 2020. Led by UT Austin, the new award will build on the trajectory of the past five years and…
This very cool paper proposes an intriguing idea. If you use a small batch size, you can fine-tune LLMs with SGD or Adafactor (algorithms with very small memory overhead). But there is a small trap: Storage precision. Let's explore that. 🧵
This very cool paper proposes an intriguing idea. If you use a small batch size, you can fine-tune LLMs with SGD or Adafactor (algorithms with very small memory overhead). But there is a small trap: Storage precision. Let's explore that. 🧵
Hierarchical RL!!!!
clickbait title
before we got fancy optimizers like muon shampoo and conditioner with matmuls , optimizer steps were essentially entirely bandwidth-bound pointwise ops in this regime more optimizer steps by definition decreases your MFU, and vice-versa if the speed increases more than your…
before we got fancy optimizers like muon shampoo and conditioner with matmuls , optimizer steps were essentially entirely bandwidth-bound pointwise ops in this regime more optimizer steps by definition decreases your MFU, and vice-versa if the speed increases more than your…
(only tangentially related): Many interesting claims in empirical DL, including my own, are “not even wrong” — they are not stated precisely enough to even be hypothesis-tested. This is often by necessity: we don’t know formal definitions of the relevant objects. (Cont)
(only tangentially related): Many interesting claims in empirical DL, including my own, are “not even wrong” — they are not stated precisely enough to even be hypothesis-tested. This is often by necessity: we don’t know formal definitions of the relevant objects. (Cont)
TRI's latest Large Behavior Model (LBM) paper landed on arxiv last night! Check out our project website: toyotaresearchinstitute.github.io/lbm1/ One of our main goals for this paper was to put out a very careful and thorough study on the topic to help people understand the state of the…
Why you should stop working on RL research and instead work on product // The technology that unlocked the big scaling shift in AI is the internet, not transformers I think it's well known that data is the most important thing in AI, and also that researchers choose not to work…
we are from New York

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