从数据、内存、计算三方面统一梳理LLM训练效率优化策略，系统总结前沿方法
arXiv:2606.10706v1 Announce Type: cross Abstract: Resource constraints increasingly determine what can be trained, fine-tuned, and deployed in large l…
重磅研究：代码领域的缩放定律显示需要比自然语言多几个数量级的数据才能达到相同性能提升，引发对大模型训练数据效率的重新思考。
arXiv:2510.08702v2 Announce Type: replace Abstract: Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws …
探讨如何借鉴语言习得装置，通过合成语言预训练提升大模型的数据效率，为AI发展带来新思路。
arXiv:2605.16758v1 Announce Type: new Abstract: Large Language Models (LLMs) remain substantially less data-efficient than humans. Pre-pretraining (PP…
提出双难度感知自进化方法，解决强化学习训练数据稀缺与动态难度转移的挑战。
arXiv:2605.17037v1 Announce Type: new Abstract: Reinforcement learning (RL) has demonstrated potential for enhancing reasoning in large language model…