Daniel Tan's Shortform

Daniel Tan

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Will future LLMs be latent reasoners? Discussion of tiny reasoning models

There's a recent paper making waves on ML twitter: https://arxiv.org/abs/2510.04871v1

tl;dr they find that a very small (7M params) latent reasoning model can outperform much larger models on simple puzzle benchmarks as well as ARC AGI

How to update on this re: whether future LLMs will be latent-reasoners?

The paper shows that small specialist models can outperform large generalist models on niche tasks. I think I would have weakly predicted this before but I am still surprised by the effect size (such a small model, such small data, etc)
Counterpoint: there's little evidence that the small specialist model can do generic tasks
Counter-counterpoint: then again, this might be solvable via scaffolding e.g. a small, powerful latent reasoning core equipped with powerful tools and knowledge retrieval might outperform a large monolith LLM

Also worth noting I was already somewhat convinced that future LLMs would be latent-reasoning-by-default: https://www.lesswrong.com/posts/4mtqQKvmHpQJ4dgj7/daniel-tan-s-shortform?commentId=ERhx3w96ueYZGzsiC

Overall a small update for me towards believing future LLMs will be latent reasoners. This has obvious implications for the monitoring agenda.

I no longer consider scaffolded LLMs as a relevant concern/threat.

I am extremely surprised to see you say that, to the point that I think I must be misinterpreting you. What tools an LLM has the ability to use seems to have huge effects on its ability to do things.

Concretely, Claude 3.5 Sonnet can do far more useful coding tasks with a single tool to execute bash commands on a VM than Claude 4.5 Sonnet can in the absence of that tool. Or is "while loop plus tools" not the type of scaffolding you're referring to?

2brambleboy1y

Yes, their goal is to make extremely parameter-efficient tiny models, which is quite different from the goal of making scalable large models. Tiny LMs and LLMs have evolved to have their own sets of techniques. Parameter sharing and recurrence works well for tiny models but increases compute costs a lot for large ones, for example.

6Jozdien1y

Given that the primary motivation for the author was how well the original HRM paper did on ARC-AGI and how the architecture could be improved, it seems like a reasonable choice to show how to improve the architecture to perform better on the same task. I agree it's a small amount of evidence that they didn't try other tasks, but as is the story seems pretty plausible.