🎓How I Study AIHISA

📖Read

📄Papers 📰Blogs 🎬Courses

💡Learn

🛤️Paths 📚Topics 💡Concepts 🎴Shorts

🎯Practice

🧩Problems 🎯Prompts 🧠Review

How I Study AI - Learn AI Papers & Lectures the Easy Way

Papers16

All Beginner Intermediate Advanced

All Sources arXiv

#Supervised Fine-Tuning

Step-DeepResearch Technical Report

Chen Hu, Haikuo Du et al.Dec 23arXiv

Search is not the same as research; real research needs planning, checking many sources, fixing mistakes, and writing a clear report.

#Deep Research#Atomic Capabilities#ReAct Agent

DiRL: An Efficient Post-Training Framework for Diffusion Language Models

Ying Zhu, Jiaxin Wan et al.Dec 23arXiv

This paper builds DiRL, a fast and careful way to finish training diffusion language models so they reason better.

#Diffusion Language Model#Blockwise dLLM#Post-Training

Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection

Juil Koo, Daehyeon Choi et al.Dec 15arXiv

This paper teaches robots to move their camera to a better spot before answering a question about what they see.

#Active Perception#Embodied AI#Vision-Language Models

Rethinking Expert Trajectory Utilization in LLM Post-training

Bowen Ding, Yuhan Chen et al.Dec 12arXiv

The paper asks how to best use expert step-by-step solutions (expert trajectories) when teaching big AI models to reason after pretraining.

#Supervised Fine-Tuning#Reinforcement Learning#Expert Trajectories