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Overthinking in language models can be curtailed by segment-level credit assignment, leading to a 5.4% accuracy boost on competitive math benchmarks.
LLMs can develop more consistent world models by predicting multiple tokens *and* anchoring those predictions to ground-truth hidden state trajectories, mitigating structural hallucinations.
