Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

arXiv:2511.08577v3 Announce Type: replace Abstract: Improving the reasoning abilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Looped transformers address this by performing multiple latent iterations to refine each token beyond a single forward pass. However, we identify a latent overthinking phenomenon: most token predictions are already correct after the first pass, but are sometimes revised into errors in later iterations. We ask whether selectively skipping latent iterations can improve accuracy, and reveal significan

Source: arXiv cs.CL — read the full report at the original publisher.

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