# 給 AI 初學者的論文清單

> 📖 本站完整內容索引（documentation index）：[llms.txt](/llms.txt)

> 原作者：Xiuyu Li (@sheriyuo) · 策展與摘要：EasyVibeCoding · 平台：X (Twitter) · 熱度：🔥 · 日期：2026-04-18

> 原始來源：https://x.com/sheriyuo/article/2045365552848482680

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## 中文摘要

# 給 AI 初學者的論文清單

這份清單來自我的指導教授易明洋（Prof. Mingyang Yi）所編寫的閱讀指南，旨在幫助電腦科學（CS）或數學系大二學生入門機器學習（ML）與強化學習（RL）。

### 基礎篇 (Foundations)

- Deep Residual Learning for Image Recognition

- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

- Layer Normalization

- Attention Is All You Need

### AI 基礎設施 (AI Infrastructure)

- An Introduction to Variational Autoencoders

- Language Models are Unsupervised Multitask Learners

- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

- Transformers for Image Recognition at Scale

- Learning Transferable Visual Models From Natural Language Supervision

- LoRA: Low-Rank Adaptation of Large Language Models

- Let's Verify Step by Step

- Reflexion: Language Agents with Verbal Reinforcement Learning

- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

### 推論加速 (Inference Acceleration)

- Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

- Linformer: Self-Attention with Linear Complexity

- Fast Inference from Transformers via Speculative Decoding

- EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

### 強化學習 (Reinforcement Learning)

- Training language models to follow instructions with human feedback

- Token-level Direct Preference Optimization

- A General Theoretical Paradigm to Understand Learning from Human Preferences

- Trust Region Policy Optimization

- Proximal Policy Optimization Algorithms

- High-Dimensional Continuous Control Using Generalized Advantage Estimation

- Asynchronous Methods for Deep Reinforcement Learning

- It Takes Two: Your GRPO Is Secretly DPO

- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

## 標籤

研究論文, 教學資源, Machine Learning, Reinforcement Learning