Max Simchowitz

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@max_simchowitz

Assistant Professor @mldcmu. Formerly: Postdoc @MITEECS, PhD @Berkeley_EECS, Math Undergrad @Princeton. New to Twitter. msimchowitz.github.io

Joined May 2024

168 Following 3.1K Followers 184 Posts

Latest posts 5

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Max Simchowitz @max_simchowitz Aug 11

Personal Update: I'll be splitting my responsibilities between CMU and Amazon FAR over the next year. I'm convinced we need a unified understanding of how the axes of scale, data, optimization and control interact for robot learning, and am excited for my lab + FAR to push towards uniting the "science of robot learning" with the "science of deep learning" at scale.

Please reach out if you are looking for a full-time position building powerful multimodal AI models for deployment on hardware, and if you are intern looking to tinker and do good science on architectures, optimizers, data, tokenization, VLAs, WAMs and more!

Max Simchowitz @max_simchowitz Nov 4

V important paper lead by @s_zhanyi with @SongShuran . TL;DR robotics need better metrics to eval pre-trained checkpoints. Perplexity works great in LLM world (+ correlates w/ posttraining perf) but for robotics, we still lack an analogue. Zhanyi's paper takes a great first step

Zhanyi Sun @ CoRL Verified account @s_zhanyi Nov 3

Can more pretraining improve a robot policy’s success rate, yet makes it worse for porttraining? 🤔

In our new work, we find that it can. Continued supervised pretraining of generative robot policies can improve reliability at the expense of coverage, and this tradeoff affects downstream posttraining.

Read on to see why this happens and how we can preserve coverage. 1/n 🧵

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Max Simchowitz @max_simchowitz Oct 26

"we're living in the mean-field limit" - my postdoc, @ThomasTCKZhang , on (the absence of) relative differences in human excellence apropos recent OAI results

Max Simchowitz @max_simchowitz Oct 21

This is one of my favorite projects I’ve been a part of this year. It’s ostensibly about robotics, but I feel that I learned something very fundamental about how AI models should represent all modalities that (superficially!) seem like they are best suited to being represented in continuous-valued (e.g. float) vectors. TL;DR discrete parametrizations are awesome, learn geometry from data, but aren’t constrained by it, and can perform better as a result. Exactly what the bitter lesson tells us we should look for.

zeji yi Verified account @ZejiYi Oct 20

Common belief: Robot policies need continuous action heads; discrete actions are 𝐥𝐞𝐬𝐬 𝐧𝐚𝐭𝐢𝐯𝐞 𝐚𝐧𝐝 𝐥𝐞𝐬𝐬 𝐩𝐫𝐞𝐜𝐢𝐬𝐞.

❗We find policies w/ the simplest discrete representations 𝐦𝐚𝐭𝐜𝐡 𝐨𝐫 𝐛𝐞𝐚𝐭 continuous ones—in sim & real, in single-step & diffusion/flow settings.

Here's why 🧵

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Max Simchowitz @max_simchowitz Oct 1

There seems to be fear reasoning models will trivialize research on robot learning architectures, algorithms, etc...

I disagree. Innovation comes from constraints, and robotics imposes a ton of then. Our new paper, led by @DashoraNitish , suggests a really promising new direction for meeting the constraints of real robot deployment whilst capturing the "intelligence" of agentic reasoning models. Check it out!

Nitish Dashora @ CoRL Verified account @DashoraNitish Oct 1

There’s lots of buzz around agentic harnesses for robots, particularly for long horizon tasks that require complex reasoning and memory. But what will it really take to turn a reasoning agent into a reactive, reliable and low-latency robot policy?

In our new paper, workspace models, we design a new memory architecture that acts as a latent harness for stronger reasoning models. Here’s why we think this might be the way forward:

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