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Research2026-05-12

On the Overscaling Curse of Parallel Thinking: System Efficacy Contradicts Sample Efficiency

Source: Arxiv CS.AI

arXiv:2601.21619v2 Announce Type: replace-cross Abstract: Parallel thinking improves LLM reasoning through multi-path sampling and aggregation. In standard evaluations, due to a lack of sample-specific priors, all samples share a global budget chosen to maximize dataset accuracy. However, many...

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