Lil'Log
lilianweng.github.io · 1,335 words · saved by 24 readers
Document my learning notes.
Hi, this is Lilian. I’m documenting my learning notes in this blog since 2017. The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific feedback loop: an AI uses its current intelligence to improve the cognitive machinery that produces its intelligence. This feedback loop in modern AI may indicate the model rewriting its own weights…
saved by
- Yung-Hsuan Yang
- Rona Wang
- Linda
- Emma Guo
- Aryan Naik
- Anosha Rahim
- Howard Halim
- Information Flow
- Neha Desaraju
- Laerdon Kim
- Rikard Saqe
- Lydia Nottingham
related reading
- Harness Engineering for Self-Improvement | Lil'Loglilianweng.github.io
- LLM Powered Autonomous Agents | Lil'Loglilianweng.github.io
- Scaling Laws, Carefully | Lil'Loglilianweng.github.io
- Reward Hacking in Reinforcement Learning | Lil'Loglilianweng.github.io
- A (Long) Peek into Reinforcement Learning | Lil'Loglilianweng.github.io
- Illustrating Reinforcement Learning from Human Feedback (RLHF)huggingface.co
- RLHF: Reinforcement Learning from Human Feedbackhuyenchip.com
- [2510.13786] The Art of Scaling Reinforcement Learning Compute for LLMsarxiv.org
- Patterns for Building LLM-based Systems & Productseugeneyan.com
- State of RL for reasoning LLMs | A. Weersaweers.de
- RL Scaling Laws for LLMs - by Cameron R. Wolfe, Ph.D.cameronrwolfe.substack.com
- IsoCompute Playbook: Optimally Scaling Sampling Compute for RL Training of LLMscompute-optimal-rl-llm-scaling.github.io