Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding
Trains one model to compress written reasoning into special-token representations, then trains another to answer using those representations.
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arXiv:2409.08561 · Initial submission: 2024-09-13
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Continuous-state reasoning — Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.
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