Sep-Latent: Separator Tokens as Natural Compression Anchors for Implicit Reasoning
Compresses written reasoning into separator-token states by gradually reducing attention to the intervening text. Distills those states into a continuous reasoning path that learns when to stop and generate an answer.
First public: · Added to this list: October 22, 2026
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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.