Scaling Latent Reasoning via Looped Language Models

Re:Scaling Latent Reasoning via Looped Language Models by oofbey on Saturday January 03, 2026 @10:38PM (#46484640)

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.

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Re:Scaling Latent Reasoning via Looped Language Models by remexre on Sunday January 04, 2026 @11:53AM (#46489733)

Or more like,

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

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Re:Scaling Latent Reasoning via Looped Language Models by oofbey on Sunday January 04, 2026 @07:43PM (#46494026)

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.

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