Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
Interleaves latent and textual tokens so a model can combine compressed internal computation with readable reasoning steps.
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arXiv:2502.03275 · Initial submission: 2025-02-05
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Thinking tokens — Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.
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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