AI RESEARCH
Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs
arXiv CS.LG
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ArXi:2509.00084v2 Announce Type: replace Test-time scaling (TTS) has gained widespread attention for enhancing LLM reasoning. Existing approaches such as Best-of-N and majority voting are limited as their performance depends on the quality of candidate responses, making them unable to produce a correct solution when all candidates are incorrect. Parallel self-refinement, generating multiple candidates and synthesizing a refined answer conditioned on them, offers a promising alternative, but the underlying mechanism driving its effectiveness remains obscure.