Vision-language models
Paper review
How image and text representations are learned together, and adapted to visual recognition tasks.
Read the vision-language reviewSelected work.
Paper reviews and study notes on how models see, plan, and act. Each figure comes from the linked article.
OpenVLA
Paper review
Visual encoders, action tokens, and adaptation in an open vision-language-action model.
Read the OpenVLA review
Monte Carlo tree search
Study notes
Selection, expansion, rollout, and backup, within a wider review of reinforcement learning.
Read the planning notes
Policy gradients and PPO
Study notes
Policy gradients, variance reduction with a critic, and the clipped PPO objective.
Read the policy optimization notesResearch interests.
Three current reading directions, with the notes behind each.
Reading notes on agent-led development, from intent and implementation to human review and deployment.
Read the agentic development notesA paper review of image and text representations, pretraining objectives, and adaptation to visual tasks.
Read the vision-language reviewA review of OpenVLA's visual encoders, action tokens, and adaptation of a robot policy.
Read the OpenVLA reviewEngineering background.
Industry experience, open-source code, and the move to AI study.
Hyundai and e-commerce.
Full-stack software development in e-commerce and at Hyundai Motor Company, followed by a move toward AI study.
Read my 2024 retrospectiveoh-my-agent.
A portable multi-agent harness for reusable skills, workflows, and coding-agent teams.
View oh-my-agentAI study in Sydney.
A retrospective on leaving industry and moving to Sydney for postgraduate study in AI.
Read my 2025 retrospectiveRecent writing.
Browse the archiveGet in touch.
Write an email© 2026 Eunkwang Shin