Beyond Imitation: Reinforcement Learning for Active Latent Planning
Uses reinforcement learning to train an active latent-planning process rather than only imitating supervised reasoning traces.
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arXiv:2601.21598 · Initial submission: 2026-01-29
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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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