<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Learning on Dylan Ler</title><link>http://dylanler.github.io/tags/learning/</link><description>Recent content in Learning on Dylan Ler</description><generator>Hugo -- 0.133.0</generator><language>en-us</language><lastBuildDate>Wed, 25 Feb 2026 10:00:00 -0800</lastBuildDate><atom:link href="http://dylanler.github.io/tags/learning/index.xml" rel="self" type="application/rss+xml"/><item><title>What I Learned Running a Long Horizon Memory Experiment on 4 A100 GPUs</title><link>http://dylanler.github.io/posts/continuous-learning-context-rot-long-horizon-memory-experiment/</link><pubDate>Wed, 25 Feb 2026 10:00:00 -0800</pubDate><guid>http://dylanler.github.io/posts/continuous-learning-context-rot-long-horizon-memory-experiment/</guid><description>I wanted to answer one practical question.
Can a model keep learning over long sessions without slowly losing grip on earlier facts?
This post is a learning oriented walkthrough of one real campaign I ran. It focuses on understanding and decision making, not just reporting scores.
Code and implementation are here:
GitHub repo: rlm-experiment-codex Live report dashboard Overview I compared two memory methods with the same base model and the same datasets.</description></item></channel></rss>