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This work trains 1B-parameter full-bandwidth transformers on up to 400B tokens and finds that latent feedback improves validation loss, 5-shot language-model evaluation, math and coding generation, and instruction-tuned performance.
This paper takes state--prediction separation to its limit with a free pause token: a prediction stream that writes no keys or values at all and so rides the sequence's existing positions, and reduces the raw flops required at inference time.
LOTUS is, to the authors' knowledge, the first latent-CoT method to bridge the gap to explicit CoT at the 3B scale, while cutting thought-phase latency by 2.5x-6.9x from compact math expressions to natural language.
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