Parallelizable Probabilistic Latent Reasoning for Diverse Reasoning Trajectories
Learns probability distributions over continuous reasoning steps from written reasoning traces, with parallel teacher-forced training. A mixture of latent strategies lets the model sample different solution paths before producing an answer.
First public: · Added to this list: October 22, 2026
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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.