329 posts in total. Keep on posting.

2026

• 03-23 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.

• 03-22 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.

• 03-21 EN

  BitNet: Scaling 1-bit Transformers for Large Language Models — In-Depth Technical Review

  An in-depth technical review of BitNet, covering 1-bit weight quantization, BitLinear layer design, scaling laws for binary Transformers, and practical deployment implications.

• 03-19 EN

  ZeRO: Shattering the Memory Wall — How DeepSpeed Trains Trillion-Parameter Models

  A technical review of ZeRO (Zero Redundancy Optimizer), analyzing how partitioning optimizer states, gradients, and parameters across data-parallel processes enables training of trillion-parameter models.

• 03-16 EN

  MetaGPT: When LLM Agents Form a Software Company — Multi-Agent Collaboration Done Right

  A technical review of MetaGPT, analyzing how encoding human software development workflows (SOP) into multi-agent systems with structured communication reduces errors in automated code generation.

• 03-14 EN

  FlashAttention: The IO-Aware Algorithm That Made Transformers Actually Fast

  A technical review of FlashAttention, analyzing how IO-aware tiling and kernel fusion achieve exact attention computation that is both faster and more memory-efficient than standard implementations.

• 03-13 EN

  LoRA: Fine-Tuning Giant Models with Pocket Change — The Low-Rank Revolution

  A technical review of LoRA (Low-Rank Adaptation), analyzing how injecting trainable low-rank decomposition matrices enables parameter-efficient fine-tuning of large language models with minimal overhead.

• 03-12 EN

  Megatron-LM: NVIDIA's Blueprint for Training Billion-Parameter Models at Scale

  A technical review of Megatron-LM's efficient large-scale training system, analyzing how tensor, pipeline, and data parallelism are composed to train trillion-parameter models on GPU clusters.

• 03-12 EN

  PaRO: Smarter Partitioning for Distributed Training — Beyond ZeRO's One-Size-Fits-All

  A technical review of PaRO, analyzing how partial redundancy optimization in data-parallel training reduces memory overhead while minimizing communication costs through selective parameter partitioning.

• 03-11 EN

  Speculative Decoding: Making LLM Inference 2-3× Faster Without Losing a Single Token

  A technical review of Speculative Decoding, analyzing how using a smaller draft model to propose tokens and a larger model to verify them achieves 2-3x inference speedup with mathematically identical output distributions.

• 03-10 EN

  InstructGPT: The RLHF Recipe That Turned GPT-3 Into a Helpful Assistant

  A detailed technical review of InstructGPT (OpenAI, 2022), analyzing how Reinforcement Learning from Human Feedback (RLHF) with a three-stage pipeline — SFT, reward modeling, and PPO — transformed next-token prediction into instruction-following behavior that aligned with human intent.

• 03-09 EN

  AutoGen: Microsoft's Framework for Building Multi-Agent Conversations That Actually Work

  A technical review of AutoGen, examining how multi-agent conversation frameworks with customizable agents enable complex LLM applications through cooperative dialogue patterns.