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Reinforcing, in ThinkWorld, those CoDs whose predictions match the actual observations more closely is typically achieved through policy optimization in reinforcement learning, for example by using the negative of the prediction error as the reward and adopting REINFORCE or preference-optimization-style objectives to increase the generation probability of high-reward sequences.
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10/28/2026
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