Diverse reasoning traces teach LLMs to make better decisions

Large language models (LLMs) are pretrained on huge volumes of unlabeled data, but afterward, they're typically post-trained on specific tasks such as instruction following, avoiding harmful outputs, and reasoning, or providing justifications for the outputs they generate. Parallel reasoning — in which multiple, diverse reasoning paths are generated and compared for the same problem — is emerging as a key tool for understanding the limits of LLMs' reasoning capability. It also underpins techniques for testing LLMs such as self-consistency, where multiple reasoning paths are aggregated to improve accuracy. LLMs are generally optimized for reasoning through supervised fine-tuning (SFT), in which each…