In the quest for super-human performance, Large Language Models (LLMs) have
traditionally been tethered to human-annotated datasets and predefined training
objectives-a process that is both labor-intensive and inherently limited. This
paper presents a transformative approach: Autonomous Learning for LLMs, a
self-sufficient learning paradigm that frees models from the constraints of
human supervision. This method endows LLMs with the ability to self-educate
through direct interaction with text, akin to a human reading and comprehending
literature. Our approach eliminates the reliance on annotated data, fostering
an Autonomous Learning environment where the model independently identifies and
reinforces its knowledge gaps. Empirical results from our comprehensive
experiments, which utilized a diverse array of learning materials and were
evaluated against standard public quizzes, reveal that Autonomous Learning
outstrips the performance of both Pre-training and Supervised Fine-Tuning
(SFT), as well as retrieval-augmented methods. These findings underscore the
potential of Autonomous Learning to not only enhance the efficiency and
effectiveness of LLM training but also to pave the way for the development of
more advanced, self-reliant AI systems.

大语言模型（LLMs）在追求超人类表现时，传统上一直依赖于人类注释的数据集和预定义的训练目标 - 这是一种既费时又固有有限的过程。本文提出了一种变革性的方法：自主学习 LLMs，这是一种无监督学习范式，使模型摆脱了人类监督的限制。我们的方法使 LLMs 能够通过与文本的直接交互来自我教育，类似于人类阅读和理解文学。我们的实证结果表明，自主学习在综合实验中表现优于预训练、监督微调以及检索增强方法。这些发现突显了自主学习不仅可以提高 LLMs 训练的效率和效果，还可以为更先进、自主的人工智能系统的发展铺平道路。