Adaptive Latent Block Insertion via Online Reward Optimization
Keeps model weights fixed and optimizes continuous input blocks before and during explicit reasoning. A sustained-uncertainty signal triggers a new block, which is updated using confidence scores from short trial continuations.
First public: · Added to this list: November 9, 2026
ACL Anthology release date. Date source.
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Published version
Adaptive Latent Block Insertion via Online Reward Optimization
Yewon Cho, Hyeongsoon Hwang, Yein Park, Hyeon Hwang, Jaewoo Kang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026, pp. 41715-41736.
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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.