AI RESEARCH
SSR: Speculative Parallel Scaling Reasoning in Test-time
arXiv CS.LG
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ArXi:2505.15340v2 Announce Type: replace Large language models (LLMs) have achieved impressive results on multi-step mathematical reasoning, yet at the cost of high computational overhead. This challenge is particularly acute for test-time scaling methods such as parallel decoding, which increase answer diversity but scale poorly in efficiency. To address this efficiency-accuracy trade-off, we propose SSR (Speculative Parallel Scaling Reasoning), a