Learning When to Think Aloud in Latent Reasoning
Learns when to write an intermediate token and when to feed a hidden state back into the model. Retains important words from each reasoning step as training anchors, then trains the mixed text-and-state policy with answer and length rewards.
First public: · Added to this list: November 9, 2026
Explore this approach
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.