I've been reading the option-option model paper by David Silver. It appears that they achieved quite an effective result. Why hasn't there been more work on it since?

It's also a relevant example for AI - one paper tested the ability of Transformers to model planetary orbits: unlike Newton's Law, the implicit forces they learn are nonsense.

https://arxiv.org/pdf/2507.06952

https://www.anthropic.com/news/detecting-and-preventing-dist...

I think the extent of distillation by Deepseek specifically is overstated. For comparison, Minimax collected over 13m 'exchanges', which starts to sound a lot more like large-scale distillation.

https://www.aisi.gov.uk/blog/our-evaluation-of-openais-gpt-5...

https://www.apple.com/newsroom/2026/06/due-to-dma-siri-ai-de...

[0]: https://www.wired.com/story/ai-pr-ed-zitron-profile/

As a punt I gave it to o3 (remember LLMs were 'bad at maths') - after 15 minutes it returned with the answer that had taken me hours.

As Ilya Sutskever has pointed out, if you read a mystery novel up until the reveal of the culprit, and then fill in "The killer was _____", don't you need to understand the novel to accurately predict the next word?

Comments URL: https://news.ycombinator.com/item?id=48354481

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=48246132

Points: 2

# Comments: 0

Two thoughts about where this could go: first, the internal world state would need to be learned to transfer to real-life robotics, since you can't query the internals of a game engine in training. Second, an enormous challenge for many of these world models is going to be truly unbounded environmental interactivity - Agora is still mostly about a few agents interacting in a static environment. Learning interaction will be hard, because the interactions in games are intentionally added in, by hand. But we (human learners) acquire a strong model for environental interaction very efficiently, which is part of what helps us generalise so effectively.

> Jax is explicitly mentioned in your pyrefly link as having a parallel (but slightly weaker) system

Jaxtyping is limited to runtime-only checks (which might as well be assert statements), and doesn't infer shapes based on operations. I'm interested in Pyrefly because I've run into the limitations of Jaxtyping in my own usage.

> Jax is built on stablehlo which uses shape dialect, which is part of the compiler (and therefore statically known).

It's true that JAX does shape inference when it compiles down to HLO - but that isn't available to the Python typing system. The Pyrefly development is addressing that, so you get static analysis before even running anything, or without having to add eval_shape calls all over your codebase. I think that's helpful, and will catch bugs. When I say Jax does inference at runtime, I mean that you have to run for the jit compiler to kick in - you don't get feedback as you edit.

> the fact that people do not know how to use the tools does not mean the tools are lacking... almost everyone that is employed to work with these tools is aware of these features and therefore eschews those kinds of comment strings.

The examples I took are from Andrej Karpathy and Noam Shazeer - maybe the disconnect is that they're more on the research side. Perhaps only unsophisticated users rely on these hacks - but as one such user I'm very excited that Pyrefly is addressing a problem I have. I suspect part of the misunderstanding that's evolved here is that these tools serve audiences with different needs.

Inference compute is definitely scaling fast, but to scale RL, training and R&D compute also needs to scale hard. I don't think it's obvious that inference will overtake R&D/training, unless there's a reputable source that states that.

As the AI labs become more reliant on enterprise adoption, it makes sense to push capabilities at a cost that makes sense for businesses. Even if it prices out consumers or hobbyists.

[0] https://epoch.ai/data-insights/openai-compute-spend