Latent Path Exploration via Potential-Guided Subtrajectory Balance
Samples continuous reasoning paths before producing an answer. Trains the paths with explicit-chain supervision, hidden-state distillation, and a balance objective that compares how much intermediate states support the correct answer.
First public: · Added to this list: November 2, 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.
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.