Large language models (LLMs) have achieved remarkable proficiency in understanding and generating human natural languages, mainly owing to the “scaling law” that optimizes relationships among language modeling loss, model parameters, and pre-trained tokens. However, with the exhaustion of high-quality internet corpora and increasing computational demands, the sustainability of pre-training scaling needs to be addressed. This paper presents a comprehensive survey of post-training scaling, an emergent paradigm aiming to relieve the limitations of traditional pre-training by focusing on the alignment phase, which traditionally accounts for a minor fraction of the total training computation. Our survey categorizes post-training scaling into three key methodologies: Supervised Fine-tuning (SFT), Reinforcement Learning from Feedback (RLxF), and Test-time Compute (TTC). We provide an in-depth analysis of the motivation behind post-training scaling, the scalable variants of these methodologies, and a comparative discussion against traditional approaches. By examining the latest advancements, identifying promising application scenarios, and highlighting unresolved issues, we seek a coherent understanding and map future research trajectories in the landscape of post-training scaling for LLMs.

We introduce OpenWebAgent, an open toolkit designed to optimize web automation by integrating both large language models (LLMs) and large multimodal models (LMMs). This toolkit focuses on enhancing human-computer interactions on the web, simplifying complex tasks through an advanced HTML parser, a rapid action generation module, and an intuitive user interface. At the core of OpenWebAgent is an innovative web agent framework that uses a modular design to allow developers to seamlessly integrate a variety of models and tools to process web information and automate tasks on the web. This enables the development of powerful, task-oriented web agents, significantly enhancing user experience and operational efficiency on the web. The OpenWebAgent framework, Chrome plugin, and demo video are available at https://github.com/THUDM/OpenWebAgent/.