Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains
Dynamically compresses portions of a reasoning chain into latent states according to the problem and intermediate context.
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arXiv:2505.16552 · Initial submission: 2025-05-22
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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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