# ML Revisited editorial principles These principles apply to the series under `content/posts/ml/ml-revisit/`. - The RL notes are one article at `rl/index.mdx`. Their Typst source and original bibliography remain under `tmp/typst-originals/`; preserve the later policy gradient revisions when consulting that older source. Keep unfinished sections marked as pending unless the author asks to complete them. Motivate REINFORCE by distinguishing the full trajectory integral from one sampled integrand value, then derive its unbiased gradient estimator once. The GAE section is a complete tutorial: introduce rollout horizons and their mixture before the estimator, keeping bias definitions in supplementary details. - The visual SSL overview is one long article at `ssl/index.mdx`, with shared SVG primitives in `ssl/scripts/elements.tsx` and styles in `ssl/styles.css`. Its former article and directory URLs redirect through `BLOG_REDIRECTS` in `src/app/utils/blog-redirects.ts`, shared by client routes and prerendering. Keep section anchors stable when reorganizing the article. - Muon diagrams distinguish the outer training step from the internal Newton–Schulz iterations. Keep the saved momentum upstream of the matrix transform and show decoupled weight decay bypassing it. The spectrum plots distinguish ideal polar factors from the actual finite-step approximation. - Write for readers with some machine-learning or LLM background who may be encountering the specific topic for the first time. The series should support both initial learning and review. Briefly establish necessary prerequisites and introduce the terms needed to follow the explanation. - Use plain language, concrete examples, and intuition to explain the problem and how the mechanism works. Connect the reasoning steps and explain what equations mean, so a first-time reader can follow the argument. Establish the problem, then introduce each prerequisite before using it in the mechanism. Do not add a heading for every reasoning step. Keep long derivations and implementation refinements in native `
` when they would interrupt the main explanation. - Preserve substantial technical content and rigor: mechanisms, assumptions, essential equations, tradeoffs, and practical implications. Make this content easier to follow through clear explanations and visual support. - Keep prose concise and focused. Remove repetition and unnecessary background while retaining the intermediate reasoning a first-time reader needs. For wording corrections, fix the specific ambiguity without expanding familiar basics into a tutorial or adding redundant examples. - Choose the medium with the greater explanatory value. Use figures generously where spatial relationships, concrete vision examples, training flows, tensor layouts, or comparisons become clearer visually; do not require a figure for every mechanism. Keep definitions, reasoning, and caveats in prose when that is more direct. Information density means useful relationships a reader can grasp, not the number of labels squeezed into a figure. For vision examples, show what the model actually sees and must predict. Derive related crops or masks from the same scene, mark their source regions, and distinguish illustrative images and values from real model outputs. In training flows, distinguish per-example predictions from batch-computed targets, show which view supervises which prediction, and identify where gradients stop. Use short prose to explain assumptions and what to notice; avoid repeating every visible step. Keep essential equations in the main flow and put longer derivations or implementation refinements in `
`. Preserve rigor and attribution, and provide meaningful figure descriptions for Markdown exports and accessible reading. Follow the [Post Diagrams conventions](https://github.com/pufanyi/blog/blob/main/content/AGENTS.md#post-diagrams).