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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.
Transformers with Temporal Middle-Layer Recurrence (T2MLR), a transformers-based latent reasoning architecture that fuses a cached middle layer representation from the previous token directly into an earlier layer of the current token position, enabling abstract intermediate computation to persist across decoding steps...
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