LLM

Agents Since OpenAI released ChatGPT in October 2022, and with the subsequent emergence of projects such as AutoGPT and AgentGPT, LLM-related agents have gradually become a research hotspot and a promising direction for practical applications in AI in recent years. This article will introduce the basic concepts of agents, their core technologies, and the latest advances in their applications. Large Language Model Agents Large Language Model Agents (LLM agents) utilize LLMs as the system’s brain, combined with modules such as planning, memory, and external tools, to achieve automated execution of complex tasks. ...

Introduction In deep learning, the design of network architectures significantly impacts model performance and training efficiency. As model depth increases, training deep neural networks faces numerous challenges, such as the vanishing and exploding gradient problems. To address these challenges, residual connections and various normalization methods have been introduced and are widely used in modern deep learning models. This article will first introduce residual connections and two architectures: pre-norm and post-norm. Then, it will describe four common normalization methods: Batch Normalization, Layer Normalization, Weight Normalization, and RMS Normalization, and analyze why current mainstream large models tend to adopt an architecture combining RMSNorm and Pre-Norm. ...

DeepSeek AI recently released DeepSeek-R1 (DeepSeek-AI, 2025), whose reasoning performance on multiple benchmarks approaches the level of OpenAI’s o1 (OpenAI, 2024), marking a significant step for the open-source community in successfully replicating o1. Relevant code for R1 can be found in the huggingface’s attempt to open-source replication project open-r1. While previous research has often relied on massive amounts of supervised data to enhance the performance of Large Language Models (LLMs), the success of DeepSeek-R1 and its earlier experiment, DeepSeek-R1-Zero, powerfully demonstrates the potential of purely large-scale reinforcement learning in improving the reasoning capabilities of LLMs. This success reinforces the profound insight proposed by Richard Sutton in “The Bitter Lesson”: ...

Background The Transformer (Vaswani et al., 2017) is a model based on the encoder-decoder architecture. This model has demonstrated outstanding performance in the field of natural language processing (NLP), leading to a series of optimized models based on it, such as BERT (Devlin et al., 2018) which uses only the encoder, GPT (Radford et al., 2018) series which uses only the decoder, and subsequent large language models (LLMs) like LLaMA (Touvron et al., 2023) and GPT-4 (OpenAI et al., 2024), most of which adopt a decoder-only architecture. ...

Background With the widespread application of Large Language Models (LLMs) across various industries, enterprises and research teams face an urgent need to adapt general-purpose models to specific domains. Foundational LLMs often fail to meet deep domain-specific requirements when handling specialized tasks. For example, in the application of closed-source programming languages, existing open-source models lack sufficient understanding of their syntax and semantics, leading to poor performance in tasks such as code generation and error correction. Therefore, injecting domain knowledge and training dedicated LLMs has become a key step in enhancing development efficiency and code quality. ...