https://arxiv.org/html/2605.06663v1

It might be possible to train a big generalist that is a composition of modules, some of which can be dropped dynamically at inference time, depending on the prompt.

Eg, that they necessitated so called "rubber rooms" like these in the NYC public schools, where teachers got paid to do nothing while waiting on arbitration.

https://en.wikipedia.org/wiki/Reassignment_center

Afaict, the grand parent poster is just very wrong. You do want to cause acute stresses to your heart (cardiovascular exercise) to get it work better.

In the last step of training LLMs, reinforcement learning from verified rewards, LLMs are trained to maximize the probability of solving problems using their own output, depending on a reward signal akin to winning in Go. It's not just imitating human written text.

Fwiw, I agree that world models and some kind of learning from interacting with physical reality, rather than massive amounts of digitized gym environments is likely necessary for a breakthrough for AGI.

Human voices don't take 30 seconds to think, retrieve, research, and summarize a high quality answer. Humans are calibrated in their knowledge, they know what they understand and what they don't. They can converse in real time without bullshitting.

Frontier real time-ish LLM generated voice systems are still plagued by 2024 era LLM nonsense, like the inability to count Rs in strawberry. [1]

I'd personally love a voice interface that, constrained by the technology of today, takes the latency hit to deliver quality.

[1] https://www.instagram.com/reel/DTYBpa7AHSJ/?igsh=MzRlODBiNWF...

The intent is still great, but now we should think about writing good general rules for building programs that are essentially all AI generated. What generic wisdom leads to flexible, auditable, composable and robust apps and systems?

Comments URL: https://news.ycombinator.com/item?id=47204331

Points: 1

# Comments: 2

If you can get 99 percent of the quality for 50 percent of the cost, that is most times a good tradeoff.

It seems completely implausible.

I could believe that if a $20 sub used every possible token granted, it would cost $250. But certainly almost no one was completely milking their subscription. In the same way that no one is streaming netflix literally 24/7.

Right now I’m struggling to beat a baseline LightGBM model trained on hand-engineered expert features. My attempts at using a win probability head on top of nanoGPT, treating events as tokens, have been significantly worse. I am seeing about 65% accuracy compared to the LightGBM’s 70%. That 5% gap is huge given how stochastic the early game is, and the Transformer is easily 4 OOM more expensive to train.

To bridge the gap, I’m moving to a hybrid approach. I’m feeding those expert features back in as additional tokens or auxiliary loss heads, and I am using the LightGBM model as a teacher for knowledge distillation to provide smoother gradients.

The main priority here is personalized post-game feedback. By tracking sharp swings in win probability, or $\Delta WP$, you can automatically generate high or low-light reels right after a match. It helps players see the exact moment a play was either effective or catastrophic.

There is also a clear application for automated content creation. You can use $\Delta WP$ as a heuristic to identify the actual turning points of a match for YouTube summaries without needing to manually scrub through hours of Twitch footage.

Now I am trying to use that model to make:

1. A post game instant replay that shows the most important/pivotal moments from the most recently finished game. Some arcades have a seperate display for observers, it could work well there, or as good filler between matches on twitch streams.

2. A personalized per tournament/yearly highlights recap.

If it works well, it might be a kind of tool that generalizes well for summarizing long twitch streams for Youtube.

https://github.com/rrenaud/kq_stream_highlights

https://bmjopen.bmj.com/content/bmjopen/5/7/e008155.full.pdf