Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
Learns a stopping policy for continuous latent reasoning so the model can allocate a variable number of internal steps.
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arXiv:2511.21581 · Initial submission: 2025-11-26
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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.
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