RLM Theory Overview feat. Alex L. Zhang | long context + REPL + sub-agents

the RLM method for improving LLM for long context tasks has been making waves recently because of it’s simplicity and the fact it just works. I’ve covered the methods in depth here with the information from the paper + I’ve interviewed the first author Alex L. Zhang to ask him all my lingering questions! enjoy!🌹 📌 also learn to code from full-stack to AI with Scrimba https://scrimba.com/?via=yacineMahdid (extra 20% off pro with my link, great resource, I love the team) # important links: 👉 first author: https://x.com/a1zhang 👉 blog: https://alexzhang13.github.io/blog/2025/rlm/ 👉 main paper: https://arxiv.org/abs/2512.24601v1 👉 RLM hands on with neural AVB: https://www.youtube.com/watch?v=nxaVvvrezbY # Table of Content - introduction: 0:00 - overview: 2:23 - long benchmarks: 13:20 - method overview: 18:35 - baseline used: 24:38 - main results: 27:00 - example trajectories: 31:30 - what failed!!!: 36:28 - interview with Alex L. Zhang: 38:43 - conclusion: 2:12:04 btw this was taken as part of our weekly deep learning study session, full stream is here: https://youtube.com/live/XE53pwDipUc and big thanks to all the subscribers for supporting this 💖 --- Join the newsletter for weekly AI content: https://yacinemahdid.com Join the Discord for general discussion: https://discord.gg/QpkxRbQBpf --- Follow Me Online Here: Twitter: https://twitter.com/yacinelearning LinkedIn: https://www.linkedin.com/in/yacinemahdid/ --- Have a great week! 👋