r/MachineLearning rules, stats, and what to post

This is a community for machine learning discussion, and the description makes the boundaries very clear. Beginners are pointed to /r/mlquestions or /r/learnmachinelearning, AGI talk goes to /r/singularity, career advice goes to /r/cscareerquestions, and datasets go to r/datasets. What stays here is substantive machine learning content for a mixed audience, and the rules say the user base includes beginners interested in ML as well as far more experienced, technical individuals with practical experience. Research papers are welcome, but only when the person sharing them adds commentary or analysis that starts a community-wide discussion. Questions are welcome too, but only from people who clearly put in time researching ML concepts and effort into communicating what guidance they need from industry practitioners.

Five rules decide whether a marketing-adjacent post survives here. “No Spam” is the baseline, and repeat offenders get permanently banned. “No Self-Promotion” says paid products cannot be promoted outright, but a post with a link to a paid product is acceptable if it offers sufficient value to community members and the intent is to share a resource or collect feedback for an open dialogue, with the decision made entirely at the discretion of the moderator team. “No Marketing Campaigns (SEO)” is the harshest one: strategic marketing campaigns and posts intended to rank for SEO get a perpetual ban, and all past posts and comments are purged entirely from the subreddit. “No arXiv Links without Body Text” means any research paper you share needs commentary or analysis to start a discussion, or it gets removed as low effort. “No Low-Effort, Beginner Questions” sends easily-searched questions elsewhere, and “No Disrespectful Behavior” reminds you the room holds both beginners and deeply experienced technical people.

A good post here leads with substance and invites discussion. If you built something, write about the technical work: the problem, the approach, what worked, what did not, and then invite feedback in an open dialogue. If you link a paid product, make sure the post offers sufficient value to community members and reads as sharing a resource or collecting feedback, not as an ad, and remember the moderator team decides this entirely at their discretion. If you share a research paper, add your own commentary or analysis so the community has something to discuss. If you ask a question, show you researched it first and be specific about the guidance you need from practitioners. Keep the tone cordial and professional, because you are writing for both beginners and experienced technical people.

Do not drop a bare arXiv link with nothing around it, because that is an explicit removal reason. Do not post a paid product with the intent to promote it, because that breaks the self-promotion rule even if the link itself is technically allowed. Do not run a marketing campaign or write posts meant to rank for SEO, because the penalty is a perpetual ban plus the purge of everything you have ever posted here. Do not ask a question you could have answered with a quick search, and do not be rude to beginners or to experts, since the rules expect cordial and professional behavior from everyone.

The purge rule is the one that stops me cold here. If you run an SEO-style campaign and get caught, every past post and comment you made in this subreddit gets wiped, so one bad move erases years of good standing. I also like that the self-promotion rule is written as a real trade: you can link a paid product, but only if the post genuinely helps people and invites open discussion. That is exactly how I think about respectful Reddit marketing, and this community just says it out loud.