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...
arxivpapers