Deep Learning

Background Recently, the number of parameters in large models has been continuously increasing, from the initial billions to today’s hundreds of billions or even trillions. While large models have brought unprecedented application effects, they have also triggered a series of severe challenges in computing resources, memory management, and training stability. Therefore, this blog summarizes some commonly used distributed parallel training and memory management techniques, hoping to help everyone better train and optimize large models. ...

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. ...

Note: This article is currently being updated. The content is in draft version and may change. Please check back for the latest version. Notations Symbol Meaning \(s, s', S_t, S_{t+1}\) State, next state, state at time \(t\), state at time \(t+1\) \(o, o_t\) Observation, observation at time \(t\) \(a, a', A_t, A_{t+1}\) Action, next action, action at time \(t\), action at time \(t+1\) \(r, r_t\) Immediate reward, reward at time \(t\) \(G_t\) Return at time \(t\) \(R(\tau)\) Return of a trajectory \(\tau\) \(\mathcal{S}\) Set of all possible states \(\mathcal{A}\) Set of all possible actions \(\mathcal{R}\) Set of all possible rewards \(\pi(a\mid s), \pi_\theta(a\mid s)\) Policy (stochastic), parameterized policy \(\mu(s), \mu_\theta(s)\) Policy (deterministic), parameterized policy \(\theta, \phi, w\) Policy or value function parameters \(\gamma\) Discount factor \(J(\pi)\) Expected return of policy \(\pi\) \(V_\pi(s)\) State-value function for policy \(\pi\) \(Q_\pi(s,a)\) Action-value function for policy \(\pi\) \(V_*(s)\) Optimal state-value function \(Q_*(s,a)\) Optimal action-value function \(A_\pi(s,a)\) Advantage function for policy \(\pi\) \(P(s'\mid s,a)\) Transition probability function \(R(s,a,s')\) Reward function \(\rho_0(s)\) Start-state distribution \(\tau\) Trajectory \(D\) Replay memory \(\alpha\) Learning rate, temperature parameter (in SAC) \(\lambda\) Eligibility trace parameter \(\epsilon\) Exploration parameter (e.g., in \(\epsilon\)-greedy), clipping parameter (in PPO) What is Reinforcement Learning? Definition ...

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. ...