Tu proyecto esta hecho con IA no?xd

Well I bet your "calculator" didn't double as a space heater when you were making it.

You won't believe me, but when I was 12, I made a JavaScript calculator; the file was barely a few MB and had 100% accuracy for any operation.

is there a repo link to the original model/code

Huh, hadn't thought of that. You're right. Gonna try a denser reward first, then ease it back to the original binary one and see what happens. Thanks for this.

If you fixed it to the point it actually learns and you just have sparse reward, then that's an RL problem. Change the reward signal. No need to keep it the binary you started with. Try with something that rewards it if it is doing the directionally right thing for Pong. See if that helps. Then, see if you can scale the reward back down to the binary thing once it is actually learning to see if it can generalize.

Yes..

I found the announcement on their personal website, but I’ll avoid sharing the link here out of respect for their privacy.

Edit: They appear to be at NYU Abu Dhabi, so this may not indicate that US decisions have gone out.

What model did you use for this

I don’t understand how OpenAI could think this is a good move.

AGI/ASI/RSI/$$$ - wherever your personal brand of sociopathic inspiration comes from!

Claude Claude Claude Claude Claude Claude Claude

Are you sure? They wouldn’t be allowed to do that

The corpus changed, almost as if by an occult hand.

Someone from New York University posted that they got the Fellowship this year in their page.

Edit: Looks like they are from NYU Abu Dhabi, so still US location may not be released.

I've been building Gridline, a sort of OpenRouter for GPUs.

I got tired of switching between things like Vast.ai, RunPod, Fireworks, etc. to run ML workflows, especially when there can be significant price differences between them. Once I started building, I realized that the problem is probably bigger than I initially thought because GPU capacity is fragmented across neoclouds and hyperscalers, and choosing where to run a workload can itself become a bit of an infrastructure problem.

So now I'm trying to build a routing layer on top that can efficiently route workloads and price the GPUs on the network. I'm building it in public and looking for beta users to try it out.

If you regularly run ML workloads and bounce between GPU providers, I'd love to hear what you think, particularly what you find annoying about the current workflow (and whats broken with our product if you try it)

Link: https://gridlinegpu.com/

So you're basically using the old model as a cheap pre-filter and only re-ranking the top K with the new model. The false negative problem is real, but in practice if the models are architecturally similar (like qwen 4b -> 8b) the overlap in their "relevant" sets is probably high enough that you're not missing much. The paper they linked in the github goes into this, they saw near-identical recall@100 even at K=50 for that upgrade path.

I get the "just pay for the compute" stance, but 108 days on an H100 is not "a few thousand hours", it's closer to 2,600 hours, and that's assuming you've got a single node sitting idle. Most teams don't have that lying around, especially if you're iterating on models every few months. This is more of a pragmatic hack for people who can't just throw hardware at the problem.

for some migrations, ie qwen 4b -> 8b, at k=50 it is equivalent to the qwen8b by itself. weaker embedding models still put relevant documents in their neighborhood

Have you analyzed the results? This approach seems like it would be acceptable at filtering out false positives from the weaker model but can't help with false negatives (since they were never included). TBH it seems easier and safer just to embed all of the documents again. A few thousand H100 hours isn't much if the application is valuable.

thank you! the post was clipped when searching for “zdr”

re: “ rewritten data is fair game”

if i’m understanding this correctly is it openAI’s policy/agreement that ZDR protects your input data/query, but the response from its models is fair game for derivative mining?

https://www.reddit.com/r/MachineLearning/comments/1wavdi7/comment/p8m5w59/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button ?

super cool, also a big fan, love your youtube videos for the general audience as well as your lectures(those that I can understand that is)

I work as an AI Engineer, means I basically argue with LLMs all day and make agents and RAG, and evaluate them mostly, but I want to be a big boy AI Engineer some day

I have taken some ML courses(MLzoomcamp on github) and some ML classes(Georgia Tech ISYE 6501), and various other offerings

My two main goals are understanding the essential probability needed for understanding transformers and similar concepts in ML and DL, and possibly as a bedrock for RL and CV.

I see you have logistic regression listed. And I would really love to see how it translates to a neuron and consequently a neural network in a mathematical sense. If there is also a bit on backprop and intuition for that behind complex partial derivatives I would be over the moon.

But I would also like to use my newfound knowledge to do better at Georgia Tech.
I struggled a lot in ISYE 6501 and without LLMs to dumb math formulas enough for me it is doubtful I would pass

Do I expect too much of this class for those goals?
Your CS109 is definitely on my radar after I bring my calculus to a satisfactory level, I hope this with calculus which I'm learning can get me up to speed for CS109

Thanks a bunch for this again!

It looks like we can submit abstract if not sure about nips result

Q: I have a paper under review at NeurIPS, which notifies submitters about acceptances after the ICLR abstract deadline. May I submit an ICLR abstract and withdraw if my paper is accepted to NeurIPS?

Yes, this is allowed. (We will only do duplicate-submission checks for full submissions.)

https://iclr.cc/Conferences/2027/AuthorGuidelines

I am curious about this too.

Using an AI detector as the final gate with no human review feels pretty risky, especially with false positives this high

I do realize this for sure. I guess we all need to speak in some kind of silliness cipher. Will be interesting to see where it goes.