Machine Learning Forums: 15 Biggest Subreddits (2026)
The biggest machine learning forums in 2026 are subreddits. Compare 15 of them by subscriber count, research focus, and self-promo rules. Reddit is home to some of the most technically rigorous machine learning communities on the internet. Researchers and practitioners share paper discussions, implementation details, and honest assessments of new techniques. The speed at which new ML papers are discussed and critiqued on Reddit makes it an essential resource for anyone working in the field.
The largest machine learning community on this list is r/MachineLearning with 2,800,000 members. 0 of the 15 are rated High for self-promotion, 11 Medium and 4 Low, so check the tolerance column before you post a link. Member counts change daily, so open each community to see the live figure.
9.2M
Combined members
15
Communities
r/MachineLearning
Largest
Promo tolerance
The ranking
Top 15 Machine Learning Subreddits, Ranked by Members
Every community on this list, sorted by member count, with its self-promotion tolerance and the content it rewards.
| Rank | Subreddit | Members | Self-promo | Best content type |
|---|---|---|---|---|
| #1 | r/MachineLearning | 2,800,000 | Low | Research papers and technical discussions |
| #2 | r/datascience | 2,750,000 | Low | Career advice and project discussions |
| #3 | r/artificial | 1,310,000 | Low | News, research summaries, and discussions |
| #4 | r/LocalLLaMA | 781,000 | Medium | Benchmarks, guides, and fine tuning results |
| #5 | r/learnmachinelearning | 662,000 | Medium | Tutorials, resources, and project feedback |
| #6 | r/deeplearning | 242,000 | Medium | Research, implementations, and tutorials |
| #7 | r/computervision | 157,000 | Medium | Research, models, and applications |
| #8 | r/MLQuestions | 110,000 | Low | Questions and detailed answers |
| #9 | r/generative | 98,000 | Medium | Generated outputs and model discussions |
| #10 | r/reinforcementlearning | 85,500 | Medium | Research, implementations, and questions |
| #11 | r/LanguageTechnology | 64,600 | Medium | Research, tools, and datasets |
| #12 | r/MLOps | 36,400 | Medium | Architecture discussions and tool reviews |
| #13 | r/tensorflow | 31,800 | Medium | Tutorials, models, and troubleshooting |
| #14 | r/pytorch | 26,700 | Medium | Code examples, tips, and troubleshooting |
| #15 | r/kaggle | 23,200 | Medium | Competition solutions and EDA notebooks |
Read this first
What Marketers Get Wrong About Machine Learning on Reddit
ML subs are split between researchers and applied practitioners. Both crowds reward papers, code, and ablation studies, not product launches.
Calling a basic API wrapper an "ML platform" gets called out within an hour by people who read the model card.
Repro of a paper with your own data or an ablation showing what each piece contributes
Title templates
Post Title Templates That Work in Machine Learning Subreddits
Steal these openers verbatim. Each one mirrors a thread pattern that consistently passes the early-vote filter in machine learning communities.
- 1
“Fine-tuned a 7B model on 40K domain-specific examples. Validation loss looked great. Production results were worse than the base model. Post-mortem.”
Fine-tuning post-mortems are among the highest-upvoted content on r/MachineLearning because they document the failure modes that papers never include. 'Worse than base model' is the specific failure pattern that every practitioner has hit but rarely writes up.
- 2
“Attention really is all you need, but not for the reasons you think. A practitioner's reading of the original paper after shipping three transformer-based products.”
r/MachineLearning tolerates revisionist takes on foundational papers only when they come from demonstrable production experience. The 'three products' claim is the credential that earns the opinion.
- 3
“Our RAG system retrieves the right chunks 94% of the time. Here's why it still gives wrong answers.”
Retrieval accuracy vs answer accuracy is one of the most discussed failure modes in applied LLM work right now. This post promises a specific and practical explanation of the gap, which is what r/LanguageTechnology and r/MachineLearning both want.
- 4
“Benchmarked five quantization methods on the same model. Results differed way more than I expected. Full comparison.”
Quantization comparison posts do consistently well on r/LocalLLaMA because the members are running local inference at varying hardware constraints. 'More than I expected' signals that the poster had a prior and updated it, which is the scientific framing the sub rewards.
Avoid removals
3 Mistakes That Get Machine Learning Posts Removed
These are the patterns mods in machine learning subs flag fastest. Spot them in your own draft before you hit post.
Using 'hallucination' as a standalone explanation in r/MachineLearning
The sub has a vocal contingent who reject 'hallucination' as a mechanism-free description that tells you nothing about why the model generated incorrect output. Using it without a mechanistic explanation signals shallow engagement with the technical literature.
Instead: Be specific about the failure mode: distribution mismatch between training and inference, retrieval failure in your context window, instruction-following breakdown on long documents. The vocabulary you use signals whether you've worked with the system or just read about it.
Posting 'AGI is three years away' takes on r/MachineLearning
The sub explicitly discourages speculative AGI timeline posts that aren't grounded in technical analysis of specific capability benchmarks. These threads get locked quickly, and your account gets tagged as a hype contributor, which makes future substantive posts harder to land.
Instead: If you want to discuss capability trajectories, anchor to specific benchmarks, specific papers, and specific architectural choices. 'Scaling laws suggest X, but the compute cost per capability improvement is growing at Y rate, which implies Z' is a technical argument. 'AGI by 2027' is not.
Asking r/MachineLearning for model recommendations without sharing evaluation criteria
The sub is primarily a research community. 'What model should I use for my chatbot' posts get redirected to r/learnmachinelearning because they don't engage with the research-level tradeoffs the sub is interested in. They're also unanswerable without constraints.
Instead: Frame as an engineering decision with explicit tradeoffs: 'For a real-time classification task with 50ms latency budget and 98% recall requirement, I'm choosing between distilBERT and a fine-tuned Phi-3-mini. Here's my evaluation methodology.' That's a post r/MachineLearning will engage with.
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After you find the subreddits
Found your Machine Learning subreddits? Now post without getting removed
A list of subs is step one. MediaFast turns it into a daily plan: which sub, which post, which thread to comment in, all checked against each community's rules.
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Field note, machine learning subreddits
The ML researcher who got a paper co-authorship from a r/MachineLearning comment
A research engineer at a mid-sized ML startup left a detailed comment on a r/MachineLearning thread about a new attention mechanism paper. The comment identified a specific evaluation flaw: the benchmark the paper used didn't account for the positional encoding scheme the authors claimed to improve. The comment was three paragraphs, technically precise, and referenced two other papers. A PhD student from the original research group saw it, realized the critique was valid, and reached out to run a corrected evaluation. Six months later, the engineer was listed as a co-author on the follow-up paper.
Takeaway
On r/MachineLearning, a technically rigorous comment on someone else's work can matter more than your own post. The community is small enough that researchers are reading the top comments on their own paper threads.
Community by community
How to Post in Each Machine Learning Subreddit
Same order as the ranking above. What each community is about, what it rewards, and one tip before your first post.
The largest and most respected machine learning subreddit, known for its paper discussions, research threads, and technical depth. Researchers from top labs frequently participate in discussions.
Best content type
Research papers and technical discussions
Posting tip
Use the correct post flair (Research, Discussion, Project, News) and provide a clear summary when sharing papers.
While broader than pure ML, this subreddit has extensive machine learning discussions in the context of real world data science projects and career development.
Best content type
Career advice and project discussions
Posting tip
Connect ML techniques to business outcomes when sharing project experiences, as this community values practical impact.
A broad AI community that covers machine learning advances alongside general AI news. Good for reaching a wider audience with ML related content that has broader implications.
Best content type
News, research summaries, and discussions
Posting tip
Make technical content accessible by explaining the practical implications of research breakthroughs for a general audience.
Focused on running large language models locally, this community bridges ML research and practical deployment. Members discuss quantization, fine tuning, and hardware optimization.
Best content type
Benchmarks, guides, and fine tuning results
Posting tip
Include complete hardware specifications, quantization details, and inference speed benchmarks when sharing local LLM setups.
A beginner friendly community for learning machine learning fundamentals. Members share learning resources, ask questions about concepts, and get feedback on their first projects.
Best content type
Tutorials, resources, and project feedback
Posting tip
Share structured learning paths or project tutorials that build understanding incrementally from basic to advanced concepts.
Focused specifically on deep learning, covering neural network architectures, training techniques, and the latest advances in areas like transformers and diffusion models.
Best content type
Research, implementations, and tutorials
Posting tip
Share implementations of recent deep learning papers with clear training details and reproducibility instructions.
Dedicated to computer vision research and applications, including object detection, image segmentation, and 3D vision. Members discuss the latest models and share implementation tips.
Best content type
Research, models, and applications
Posting tip
Share visual results alongside quantitative metrics when presenting computer vision projects.
Dedicated specifically to asking and answering machine learning questions. No question is too basic, and the community is patient with learners at all levels.
Best content type
Questions and detailed answers
Posting tip
Provide thorough answers with mathematical intuition and code examples when helping others understand ML concepts.
Covers generative models including GANs, VAEs, diffusion models, and their creative applications. Members share generated outputs alongside technical discussions about model architecture.
Best content type
Generated outputs and model discussions
Posting tip
Share your generation process including model choice, training data considerations, and any custom modifications you made.
Covers reinforcement learning theory, algorithms, and applications. Topics range from classic RL algorithms to modern deep RL approaches and their applications in robotics and games.
Best content type
Research, implementations, and questions
Posting tip
Share RL environment implementations or training results with clear reward function descriptions and hyperparameter details.
Focused on natural language processing research and tools. Covers topics like transformers, text classification, named entity recognition, and language model fine tuning.
Best content type
Research, tools, and datasets
Posting tip
Share NLP model evaluations across multiple benchmarks and discuss where your approach succeeds or falls short.
Focused on the operational side of machine learning, covering model deployment, monitoring, CI/CD for ML, and production infrastructure. Essential for ML engineers working on real systems.
Best content type
Architecture discussions and tool reviews
Posting tip
Share MLOps architecture decisions with context about team size, model count, and the specific production challenges you solved.
Covers TensorFlow and Keras for building and deploying machine learning models. Topics include model architecture, TFLite deployment, and migration between TensorFlow versions.
Best content type
Tutorials, models, and troubleshooting
Posting tip
Share complete, runnable code examples that others can copy and adapt for their own projects.
The community for PyTorch users covering the framework, custom layers, training pipelines, and deployment. Members help with debugging, optimization, and best practices for production ML.
Best content type
Code examples, tips, and troubleshooting
Posting tip
Share minimal reproducible code examples when asking for help, and include your PyTorch version and hardware details.
The community for Kaggle competitors and data scientists, discussing competition strategies, dataset exploration, and effective feature engineering techniques.
Best content type
Competition solutions and EDA notebooks
Posting tip
Share detailed post competition writeups explaining your approach, what worked, and what did not.
Self-promotion
Understanding Self-Promotion Tolerance
Each subreddit has its own culture around self-promotion. Knowing the tolerance level before posting helps you avoid bans and build genuine credibility.
High Tolerance
These communities welcome product mentions and project sharing as long as you follow subreddit rules. You can include links to your product in posts and comments, but genuine value should still come first.
On this list (0)
None of the 15 communities.
Medium Tolerance
Self-promotion is allowed in specific threads or under certain conditions (like designated weekly threads). Read the sidebar rules carefully. Build some post history before sharing your own products or content.
On this list (11)
Low Tolerance
These subreddits strictly prohibit self-promotion. Focus on providing value through comments and educational posts. Build karma and credibility first. Mention your product only when directly asked for recommendations.
On this list (4)
Go further
Find Even More Subreddits for Your Machine Learning Product
This list covers the top communities, but there are hundreds more niche subreddits where your target audience hangs out. The subreddit finder from MediaFast analyzes your product and matches you with the most relevant communities, including hidden gems most marketers miss.
Related lists
Explore Related Subreddit Lists
Watch
Watch Before You Post in Machine Learning Communities
A quick tour of the subreddits programmers actually use to learn, ask questions, and follow industry discussion. Watch "Top SubReddits You Must Follow to Learn Programming | Reddits for Programmers | SCALER #shorts" by SCALER.
Video by SCALER on YouTube
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Machine Learning Subreddits FAQ
11 questions about finding and using the best machine learning communities on Reddit.
r/MachineLearning is the largest community on this list with 2,800,000 members. The largest and most respected machine learning subreddit, known for its paper discussions, research threads, and technical depth. Researchers from top labs frequently participate in discussions.
This list ranks 15 machine learning subreddits by member count, from r/MachineLearning (2,800,000 members) down to r/kaggle (23,200 members). Together they add up to 9,178,200 members, counted per community, so people who joined several are counted more than once.
Here is how the 15 communities on this list rate for self-promotion. Medium tolerance (designated threads or conditions): r/LocalLLaMA, r/learnmachinelearning, r/deeplearning, r/computervision, r/generative, r/reinforcementlearning, r/LanguageTechnology, r/MLOps, r/tensorflow, r/pytorch and r/kaggle. Low tolerance (no self-promotion): r/MachineLearning, r/datascience, r/artificial and r/MLQuestions. Rules change, so read each subreddit's sidebar and pinned posts before you share a link.
Start with r/LocalLLaMA. Among the communities on this list with the most room for self-promotion (rated Medium), it is the largest, with 781,000 members. What works there: Benchmarks, guides, and fine tuning results. Posting tip: Include complete hardware specifications, quantization details, and inference speed benchmarks when sharing local LLM setups.
Repro of a paper with your own data or an ablation showing what each piece contributes. Each community still rewards something slightly different. The three largest on this list: r/MachineLearning (Research papers and technical discussions); r/datascience (Career advice and project discussions); r/artificial (News, research summaries, and discussions).
Calling a basic API wrapper an "ML platform" gets called out within an hour by people who read the model card. The patterns moderators flag fastest: Using 'hallucination' as a standalone explanation in r/MachineLearning; Posting 'AGI is three years away' takes on r/MachineLearning; Asking r/MachineLearning for model recommendations without sharing evaluation criteria.
This page lists 4 title templates for machine learning communities. Two examples, "Fine-tuned a 7B model on 40K domain-specific examples. Validation loss looked great. Production results were worse than the base model. Post-mortem." and "Attention really is all you need, but not for the reasons you think. A practitioner's reading of the original paper after shipping three transformer-based products.", show the pattern. Why the first one works: Fine-tuning post-mortems are among the highest-upvoted content on r/MachineLearning because they document the failure modes that papers never include. 'Worse than base model' is the specific failure pattern that every practitioner has hit but rarely writes up.
r/MachineLearning is widely considered the most authoritative ML community on Reddit, with nearly 3 million members including researchers from top AI labs. The paper discussion threads are particularly valuable for understanding the significance and limitations of new research.
r/learnmachinelearning is specifically designed for ML beginners, offering a supportive environment for questions at any level. r/MLQuestions is also excellent for getting specific technical questions answered. Both communities are patient and encouraging with newcomers.
r/MLOps focuses specifically on deploying and maintaining ML models in production. r/dataengineering covers the infrastructure side. For framework specific deployment questions, r/pytorch and r/tensorflow have active communities that help with model serving and optimization.
Post on r/MachineLearning with the Research flair and include a clear summary of your contributions. Provide a link to the paper and any code repositories. Engage with comments and questions honestly, including acknowledging limitations. The community values transparency above all.
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