Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.
Please read the tutorial at this link. https://ebooknice.com/page/post?id=faq
We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.
For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.
EbookNice Team
Status:
Available4.7
5 reviewsGeneral reasoning represents a long-standing and formidable challenge in artifcial Received: 14 February 2025intelligence (AI). Recent breakthroughs, exemplifed by large language models (LLMs)1,2 and chain-of-thought (CoT) prompting3, have achieved considerable Accepted: 17 July 2025success on foundational reasoning tasks. However, this success is heavily contingent Published online: 17 September 2025on extensive human-annotated demonstrations and the capabilities of models are Open accessstill insufcient for more complex problems. Here we show that the reasoning Check for updatesabilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labelled reasoning trajectories. The proposed RL framework facilitates the emergent development of advanced reasoning patterns, such as self-refection, verifcation and dynamic strategy adaptation. Consequently, the trained model achieves superior performance on verifable tasks such as mathematics, coding competitions and STEM felds, surpassing its counterparts trained through conventional supervised learning on human demonstrations. Moreover, the emergent reasoning patterns exhibited by these large-scale models can be systematically used to guide and enhance the reasoning capabilities of smaller models.