Readit NewsThe huggingface spaces link doesn't work, fyi.
Sounds awesome in the demo page though.
vagabund commented on An analysis of DeepSeek's R1-Zero and R1 arcprize.org/blog/r1-zero... · Posted by u/meetpateltech
> But now with reasoning systems and verifiers, we can create brand new legitimate data to train on. This can either be done offline where the developer pays to create the data or at inference time where the end user pays!
> This is a fascinating shift in economics and suggests there could be a runaway power concentrating moment for AI system developers who have the largest number of paying customers. Those customers are footing the bill to create new high quality data … which improves the model … which becomes better and more preferred by users … you get the idea.
While I think this is an interesting hypothesis, I'm skeptical. You might be lowering the cost of your training corpus by a few million dollars, but I highly doubt you are getting novel, high quality data.
We are currently in a world where SOTA base model seems to be capped at around GPT4o levels. I have no doubt that in 2-3 years our base models will compete with o1 or even o3... just it remains to be seen what innovations/optimizations get us there.
The most promising idea is to use reasoning models to generate data, and then train our non-reasoning models with the reasoning-embedded data. But... it remains to be seen how much of the chain of thought reasoning you can really capture into model weights. I'm guessing some, but I wonder if there is a cap to multi-head attention architecture. If reasoning can be transferred from reasoning models to base models, OpenAI should have already trained a new model with o3 training data, right?
Another thought is maybe we don't need to improve our base models much. It's sufficient to have them be generalists, and to improve reasoning models (lowering price, improving quality) going forward.
> I highly doubt you are getting novel, high quality data.
Why wouldn't you? Presumably the end user would try their use case on the existing model, and if it performs well, wouldn't bother with the expense of setting up an RL environment specific to their task.
If it doesn't perform well, they do bother, and they have all the incentive in the world to get the verifier right -- which is not an extraordinarily sophisticated task if you're only using rules-based outcome rewards (as R1 and R1-Zero do)
vagabund commented on Genesis – a generative physics engine for general-purpose robotics genesis-world.readthedocs... · Posted by u/tomp
I saw this on twitter and actually came on HN to see if there was a thread with more details. The demo on twitter was frankly unbelievable. Show me a water droplet falling...okay...now add a live force diagram that is perfectly rendered by just asking for it? What? Doesn't seem possible/real. And yet it seems reputable, the docs/tech look legit, they just "aren't released the generative part yet".
What is going on here? Is the demo just some researchers getting carried away and overpromising, hiding some major behind the scenes work to make that video?
My understanding is they built a performant suite of simulation tools from the ground up, and then they expose those tools via API to an "agent" that can compose them to accomplish the user's ask. It's probably less general than the prompt interface implies, but still seems incredibly useful.
vagabund commented on Transformers in music recommendation research.google/blog/tran... · Posted by u/panarky
Is there a site that has hand-curated playlists I would love that let's say if I want to listen to Korean pop from the 90s or Minimal Techno from the 00s.
Searching Spotify for user created playlists is still probably your best bet. Youtube has some good results too.
Here are two that might fit what you're looking for:
'90s K-pop: https://open.spotify.com/playlist/6mnmq7HC68SVXcW710LsG0?si=...
'00s minimal techno: https://open.spotify.com/playlist/6mnmq7HC68SVXcW710LsG0?si=...
There are sites to convert from spotify to another service if you don't have it.
vagabund commented on Transformers in music recommendation research.google/blog/tran... · Posted by u/panarky
Spotify's recommendations are biased towards what you've listened to recently. Do you share the account with someone else?
No, but it's also biased toward their commercial partners. From this page [0], detailing their recommendation process:
> How do commercial considerations impact recommendations?
> [...] In some cases, commercial considerations, such as the cost of content or whether we can monetize it, may influence our recommendations. For example, Discovery Mode gives artists and labels the opportunity to identify songs that are a priority for them, and our system will add that signal to the algorithms that determine the content of personalized listening sessions. When an artist or label turns on Discovery Mode for a song, Spotify charges a commission on streams of that song in areas of the platform where Discovery Mode is active.
So Spotify's incentivized to coerce listening behavior towards contemporary artists that vaguely match your tastes, so they can collect the commission. This explains why it's essentially impossible to keep the algorithm in a historical era or genre -- even if well defined, and seeded with a playlist full of songs that fit the definition. It also explains why the "shuffle" button now defaults to "smart shuffle" so they can insert "recommended" (read: commission-generating) songs into your playlist.
[0]: https://www.spotify.com/ca-en/safetyandprivacy/understanding...
vagabund commented on Transformers in music recommendation research.google/blog/tran... · Posted by u/panarky
It's all very nice but if they end up "altering" the results heavily to play you the music they want you to listen for X or Y reason then it's pointless.
I would like to be able to run this model myself and have a pristine and unbiased output of suggestions
It may just be my perception, but I seem to have noticed this steering becoming a lot more heavy handed on Spotify.
If I try to play any music from a historical genre, it's only about 3 or 4 autoplays before it's queued exclusively contemporary artists, usually performing a cheap pastiche of the original style. It's honestly made the algorithm unusable, to the point that I built a CLI tool that lets me get recommendations from Claude conversationally, and adds them to my queue via api. It's limited by Claude's relatively shallow ability to retrieve from the vast library on these streaming services, but it's still better than the alternative.
Hoping someone makes a model specifically for conversational music DJing, it's really pretty magical when it's working well.
The notion that Scale AI's data is of secondary value to Wang seems wrong: data-labeling in the era of agentic RL is more sophisticated than the pejorative view of outsourcing mechanical turk work at slave wages to third world workers, it's about expert demonstrations and work flows, the shape of which are highly useful for deducing the sorts of RL environments frontier labs are using for post-training. This is likely the primary motivator.
> LLMs are pretty easy to make, lots of people know how to do it — you learn how in any CS program worth a damn.
This also doesn't cohere with my understanding. There's only a few hundred people in the world that can train competitive models at scale, and the process is laden with all sorts of technical tricks and trade secrets. It's what made the deepseek reports and results so surprising. I don't think the toy neural network one gets assigned to create in an undergrad course is a helpful comparison.
Relatedly, the idea that progress in ML is largely stochastic and so horizontal orgs are the only sensible structure seems like a weird conclusion to draw from the record. Saying Schmidhuber is a one hit wonder, or "The LLM paper was written basically entirely by folks for whom "Attention is All You Need" is their singular claim to fame" neglects a long history of foundational contributions in the case of the former, and misses the prolific contributions of Shazeer in the latter. Alec Radford is another notable omission as a consistent superstar researcher. To the point about organizational structure, OpenAI famously made concentrated bets contra the decentralized experimentation of Google and kicked off this whole race. Deepmind is significantly more hierarchical than Brain was and from comments by Pichai, that seemed like part of the motivation for the merger.