aiqbench takeMachine Learning

This research introduces a training method using specialized "forking tokens" to prevent mode collapse and enable large language models to generate diverse, high-quality reasoning paths for the same problem. By aggregating these distinct strategies during post-training, the approach aims to expand the effective reasoning capacity of LLMs beyond the limitations of single-trace supervised fine-tuning.

Sources coveringAmazon Science, LessWrong, The Verge, arXiv

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Diverse reasoning traces teach LLMs to make better decisions

Amazon ScienceMay 26, 20265 min read

How to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies.

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…

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