Scaling Latent Reasoning via Looped Language Models (opens in new tab)

(arxiv.org)

84 pointsremexre4mo ago15 comments

15 comments

If you squint your eyes it's a fixed iteration ODE solver. I'd love to see a generalization on this and the Universal Transformer metioned re-envisioned as flow-matching/optimal transport models.

kevmo3144mo ago

How would flow matching work? In language we have inputs and outputs but it's not clear what the intermediate points are since it's a discrete space.

Etheryte4mo ago

One of the core ideas behind LLMs is that language is not a discrete space, but instead a multidimensional vector field where you can easily interpolate as needed. It's one of the reasons LLMs readily make up words that don't exist when translating text for example.

kevmo3144mo ago

Not the input and output though, which is the important part for flow matching modeling. Unless you're proposing flow matching over the latent space?

cfcf144mo ago

This makes me think it would be nice to see some kinda child of modern transformer architecture and neural ODEs. There was such interesting work a few years ago on how neural ode/pdes could be seen as a sort of continuous limit of layer depth. Maybe models could learn cool stuff if the embeddings were somehow dynamical model solutions or something.

the84724mo ago

Does the training process ensure that all the intermediate steps remain interepretable, even on larger models? Not that we end up with some alien gibberish in all but the final step.

oofbey4mo ago

Training doesn’t encourage the intermediate steps to be interpretable. But they are still in the same token vocabulary space, so you could decode them. But they’ll probably be wrong.

the84724mo ago

token vocabulary space is a hull around human communication (emoji, mathematical symbols, unicode scripts, ...), inside that there's lots of unused representation space that an AI could use to represent internal state. So this seems to be bad idea from an safety/oversight perspective.

https://openai.com/index/chain-of-thought-monitoring/

oofbey4mo ago

What is a bad idea? Allowing reasoning to happen in continuous space instead of discrete token space? This paper can be seen as a variant of the Coconut models (continuous chain of thought). Continuous reasoning is certainly more efficient when it works. Lack of interpret ability makes certain safety systems harder to enforce. Is that your point?

1 more reply

lukebechtel4mo ago

so it's:

output = layers(layers(layers(layers(input))))

instead of the classical:

output = layer4(layer3(layer2(layer1(input))))

oofbey4mo ago

Yeah if layers() is a shortcut for layer4(layer3(layer2(layer1(input)))). But sometimes it’s only

output = layers(input)

output = layers(layers(input))

Depends on how difficult the token is.

remexreOP4mo ago

Or more like,

    x = tokenize(input)
    i = 0
    do {
      finish, x = layers(x)
    } while(!finish && i++ < t_max);
    output = lm_head(x)

oofbey4mo ago

That’s closer still. But even closer would be:

    x = tokenize(input)
    i = 0
    finish = 0
    do {
      p, x = layers(x)
      finish += p
    } while(finish < 0.95 && i++ < t_max);
    output = lm_head(x)

Except the accumulation of the stop probabilities isn’t linear like that - it’s more like a weighted coin model.

j / k navigate · click thread line to collapse

15 comments

kelseyfrog4mo ago

If you squint your eyes it's a fixed iteration ODE solver. I'd love to see a generalization on this and the Universal Transformer metioned re-envisioned as flow-matching/optimal transport models.

kevmo3144mo ago

How would flow matching work? In language we have inputs and outputs but it's not clear what the intermediate points are since it's a discrete space.

Etheryte4mo ago

kevmo3144mo ago

Not the input and output though, which is the important part for flow matching modeling. Unless you're proposing flow matching over the latent space?

cfcf144mo ago

the84724mo ago

Does the training process ensure that all the intermediate steps remain interepretable, even on larger models? Not that we end up with some alien gibberish in all but the final step.

oofbey4mo ago

Training doesn’t encourage the intermediate steps to be interpretable. But they are still in the same token vocabulary space, so you could decode them. But they’ll probably be wrong.

the84724mo ago

https://openai.com/index/chain-of-thought-monitoring/

oofbey4mo ago

1 more reply

lukebechtel4mo ago

so it's:

output = layers(layers(layers(layers(input))))

instead of the classical:

output = layer4(layer3(layer2(layer1(input))))

oofbey4mo ago

Yeah if layers() is a shortcut for layer4(layer3(layer2(layer1(input)))). But sometimes it’s only

output = layers(input)

output = layers(layers(input))

Depends on how difficult the token is.

remexreOP4mo ago

Or more like,

    x = tokenize(input)
    i = 0
    do {
      finish, x = layers(x)
    } while(!finish && i++ < t_max);
    output = lm_head(x)

oofbey4mo ago

That’s closer still. But even closer would be:

    x = tokenize(input)
    i = 0
    finish = 0
    do {
      p, x = layers(x)
      finish += p
    } while(finish < 0.95 && i++ < t_max);
    output = lm_head(x)

Except the accumulation of the stop probabilities isn’t linear like that - it’s more like a weighted coin model.

j / k navigate · click thread line to collapse