Rohan Paul Shares Paper on Amortizing Reasoning in Models

Rohan Paul, a machine learning engineer, posted a link to an arXiv paper titled Reason Wide, Not Deep: Amortizing the Reasoning Premium into Distilled Skills. The paper abstract notes that reasoning modes of language models outperform their non-reasoning counterparts on multi-step agentic tasks. However, they incur a premium in output tokens on every episode, with much of it spent re-deriving elements. The post presents this as the central idea from the research work available on the arXiv platform. This sharing brings attention to efforts aimed at making advanced reasoning more efficient in language model applications.