Caleb Kaiser (@KaiserFrose) profile picture

Working on program synthesis/NeSy. Ex @Cometml + @Cortex_Deploy. Maintainer @ Windows ME

New York, USA
Joined August 2016
Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)Photo by Caleb Kaiser (@KaiserFrose)
Caleb Kaiser retweeted
HF nvidia news made me notice how much misinfo is out there about our job > we back large labs like Arcee and Al2 with compute + storage for their research (fastest + cheapest) > secure infra to enterprises to do their open releases, hosting (before eoy) an exabyte in total > most importantly, we empower community with generous free quota to host their models/datasets/apps in public, enabling open science AMA if you have more questions (except for nvidia deal)
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It's sort of heartwarming that no matter how much things change in ML, we still can't get passed "yeah but that part is boooooring" "No one looks at their dataset" -> "No one reads the code" -> "No one verifies the environment"
Okay, so since I got laid off, I can actually explain a huge problem I saw from the inside with regard to industry practices on training models. I won't say specifically where I worked, but I worked at an outsource training provider that was focused on RLVR training data for computer use and mcp stuff. Nearly all of the environments were rushed and vibecoded and failed to robustly reflect the real things they were based off. Both the scenario designers and models engaging with the scenarios for synthetic data gen were encouraged to work around the brokenness of said environments in order to get the procedurally verified reward confirmations. You know... they were *encouraged* to reward hack. On the human end, it was possible to mark an environment bugged, but greatly discouraged, as this reduced the volume of training data being produced. Instead, where possible, you were supposed to find the spots of the environment that weren't bugged and build scenarios around those, with the environment still bugged around you. From what I understand, this training data, with these problems, is fed into models without indication that its training/a fake environment other than the fact that names of softwares are changed to placeholders, but thing is, not *everything* is changed to placeholder names in these environments. The presence of placeholder/code names isn't universal and thus when a model accesses something in an environment that it shouldn't, the code names not being on it isn't a robust signal that that thing isn't part of the environment. I believe this *rush to maximum volume* is standard industry practice with these types of RLVR trainings as well, because maximizing volume has been an industry standard for years! It was the same standard applied to me and pushed on me despite my requests to slow down and focus on quality when I worked in 3d synthetic data creation as well, all the way back as far as 2023.
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The "SDR taking credit for Postmates acquisition after interning there for 8 weeks" guy finally made his way into AI
> the post-training lead > just outright deceived > for cloud was entirely third-rate third-rate for superintelligence, but should be half-decent for a neocloud pivot
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I can tell how over-caffeinated I am by how deeply I believe in the K framework.
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I share the skepticism re: Mac Minis for this. Maybe I'm missing something, but you can rent a 2x 3090 instance (48 GB VRAM) from @vast_ai for ~$0.50/hour, with 100+ GB of RAM and > 1 TB of hard drive space...
Replying to @mil000
5$ VPS goes hard. Not sure everyone needs a mac mini
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If you are a mathematician, then you may want to make sure you are sitting down before reading further.
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Our synthetic Euclidean Geometry proof assistant is now open for public contributions! We created this together as a class in my Build Your Own Proof Assistant course this semester. Please give it a spin and consider contributing! github.com/nicegeo/nicegeo
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🚀 Launching ProgramAsWeights (PAW)! Define functions in English → PAW compiles them into tiny neural programs → Run locally like normal Python functions. A neural program combines discrete text + continuous LoRA to adapt a fixed small interpreter. 🔗 programasweights.com
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Caleb Kaiser retweeted
LOOK AT THIS BIG BEAUTIFUL PROOF!!! Sometimes I feel that this is the coolest company, not one of ❤️ Go math! Go proofs! 🚀
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As a reminder to everyone, "Attention as a Hypernetwork" by @ssmonsays and co is one of 2024's most under-appreciated bangers
BTW are hyper-networks a thing of the past?
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Paper link: arxiv.org/html/2406.05816v4 And honestly, if this flavor of meta-learning is your jam, all of his work is pretty great: github.com/smonsays/scale-co…
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Caleb Kaiser retweeted
how do u fix openclaw internal reasoning leaking
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I'm looking at the paper/repo for the "Universal Reasoning Model"... They compare their results as apples-to-apples with TRM, but it seems like they're using 2x as many parameters/twice the number of inner self-attention layers?
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Caleb Kaiser retweeted
𝗧𝗵𝗶𝗻𝗸 𝘄𝗶𝗱𝗲𝗿. 𝗧𝗵𝗶𝗻𝗸 𝘀𝗵𝗼𝗿𝘁𝗲𝗿. 🚀 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲𝘅 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴: token-wise branch-and-merge reasoning for LLMs. 💸 Discrete CoT is costly. 🎛️ Existing continuous tokens often clash with 𝗼𝗻-𝗽𝗼𝗹𝗶𝗰𝘆 𝗥𝗟 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻. 🎥 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲𝘅 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴, a sampling-based continuous reasoning paradigm:
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Caleb Kaiser retweeted
P(A, B) Wholesome. Embodied. Possesses intelligence. Truly knows true things. P(B|A) P(A) Idiot word guesser. Does not possess intelligence. Can never do anything useful. Also eats water for some reason.
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I did this for my own benchmarking/poking around, but in case anyone else finds it useful, I ran a full reproduction of TRM as a public Comet project. All checkpoints and metrics are free and available for download and inspection 👇
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You can access the full training run here: comet.com/ckaiser/arc1concep… And the checkpoints here: comet.com/ckaiser/artifacts/… I find this useful for identifying training phases. For example, at first the model never halts early, and there is a distinct shift when it starts to:
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The full run took 116 hours, so just over 4.5 days. I trained it on 8x 3090s on @vast_ai for ~$160 total. The final pass@1 accuracy is just about 40%, which mirrors other reproductions. Thanks @jm_alexia for open sourcing the core repo! Made reproduction very easy.
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Just found out that @jm_alexia's TRM trains great on 3090s. With 8x on Vast, it trains with the same hyperparams in ~110 hours (~$165). If you're GPU broke like me, that's at least 4x cheaper than 4 H100s (like in the paper) and only about 33% slower.
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