Efficient Reasoning with Hidden Thinking
Compresses visible reasoning into hidden thinking states to reduce generated reasoning length while preserving task performance.
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arXiv:2501.19201 · Initial submission: 2025-01-31
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Internalized reasoning — Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.
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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