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351 posts in total. Keep on posting.
Showing posts 289–300 of 351. Each entry opens locally on this site; legacy Hexo posts link back to their original article at the bottom for reference.
2026
- EN
Layer Pruning for Efficient Large Language Models — In-Depth Technical Review
Layer pruning removes redundant layers from LLMs to reduce compute and memory costs. Covers layer importance metrics, pruning strategies, and fine-tuning recovery.
- EN
Constitutional AI: Harmlessness from AI Feedback — In-Depth Technical Review
Constitutional AI trains harmless AI assistants using AI-generated feedback instead of human labels. Covers the critique-revision pipeline, RLAIF, and comparison with RLHF.
- EN
Chain-of-Thought Prompting Elicits Reasoning in LLMs — In-Depth Technical Review
Chain-of-Thought prompting enables LLMs to perform complex reasoning by generating intermediate steps. Covers few-shot CoT, zero-shot CoT, and analysis across arithmetic, commonsense, and symbolic tasks.
- EN
Ring Attention: Blockwise Transformers for Near-Infinite Context — In-Depth Technical Review
Ring Attention enables near-infinite context length by distributing attention computation across devices in a ring topology. Covers blockwise computation, online softmax, and memory analysis.
- EN
Mamba: Linear-Time Sequence Modeling with Selective State Spaces — In-Depth Technical Review
Mamba introduces selective state space models as an alternative to Transformers with linear-time complexity. Covers selective scan, hardware-aware algorithms, and language modeling results.
- EN
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection — In-Depth Technical Review
GaLore reduces memory requirements for LLM training through gradient low-rank projection. Covers the mathematical foundation, subspace switching, and memory savings analysis.
- EN
Alpa: Automating Inter- and Intra-Operator Parallelism — In-Depth Technical Review
Alpa automates the search for optimal parallelism strategies combining data, tensor, and pipeline parallelism. Covers the ILP formulation, inter-operator DP, and compilation framework.
- EN
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers — In-Depth Technical Review
GPTQ enables efficient post-training quantization of large language models to 3-4 bits with minimal accuracy loss. Covers the layer-wise quantization algorithm, Hessian-based error correction, and practical deployment.
- EN
Proximal Policy Optimization Algorithms — In-Depth Technical Review
PPO is one of the most influential RL algorithms. This review covers policy gradients, TRPO, the clipped surrogate objective, and PPO's role in RLHF/LLM alignment.
- 中
近端策略优化算法（PPO）— 深度阅读笔记
PPO（近端策略优化）是深度学习时代最具影响力的强化学习算法之一。本文从零开始详细讲解策略梯度、TRPO 到 PPO 裁剪目标的完整推导，覆盖 MuJoCo、Atari 实验分析，以及 PPO 在 RLHF/LLM 对齐中的核心作用。
- EN
MiRA: A Subgoal-driven Framework for Improving Long-Horizon LLM Agents — Technical Review
A detailed technical review of Google DeepMind's paper 'A Subgoal-driven Framework for Improving Long-Horizon LLM Agents', analyzing how MiRA uses milestone-based subgoal decomposition and potential-based reward shaping to overcome planning bottlenecks in long-horizon web navigation.
- EN
Attention Is All You Need: The Transformer — In-Depth Technical Review
An in-depth technical review of the Transformer architecture from "Attention Is All You Need", covering self-attention mechanics, positional encoding, multi-head attention, training details, and lasting impact.