r/mlscaling
19k members
r/mlscaling is a subreddit with 19k members. The community frequently discusses ai, scaling, models, training, and model, and the most common flair used is r, r, emp, r, rl, emp, code, and op, d.
ML/AI/DL research on approaches using large models, datasets, and compute: "more is different"
Popular Topics in r/mlscaling
#1
Ai
: "China launches customs crackdown on Nvidia Ai chips - "senior officials in Beijing have determined that domestic chips have reached performance standards that compare with Nvidia’s China-specific chips""
110 posts
#2
Scaling
: "The Art of Scaling Reinforcement Learning Compute for LLMs—Khatri, Madaan et al 2025 (extensive 400k GPU-hour exploration of how RL scales)"
66 posts
#3
Models
: "Google Research: Introducing 'Nested Learning': A new ML paradigm for continual learning | "A new approach that views Models as a set of smaller, nested optimization problems, each with its own internal workflow, in order to mitigate or even completely avoid the issue of ' catastrophic forgetting""
44 posts
#4
Training
: ""Muon is Scalable for LLM Training", Liu et al 2025 {Moonshot AI}"
35 posts
#5
Model
: ""Kimi Linear: An Expressive, Efficient Attention Architecture", Kimi Team 2025"
32 posts
#6
Deep Learning
: "Cognizant Introduces MAKER: Achieving Million-Step, Zero-Error LLM Reasoning | "A new approach shows how breaking reasoning across millions of AI agents can achieve unprecedented reliability, pointing to a practical path for scaling LLM intelligence to organizational and societal level""
23 posts
#7
Ml
21 posts
#8
Llm
: ""Muon is Scalable for Llm Training", Liu et al 2025 {Moonshot AI}"
20 posts
#9
Reasoning
18 posts
#10
Language
: ""Diffusion Language Models are Super Data Learners", Ni et al. 2025"
18 posts
Flair Used in r/mlscaling
#1
R
: "Google Research: A New Paper Suggests That LLMs Don’t Just Memorize Associations, They Spontaneously Organize Knowledge Into Geometric Structures That Enable Reasoning"
49 posts
#2
R, Emp
: "DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively, Weng et al. 2025 [20k GPU-hours, >$60k in API costs, >1k autonomous experiments, surpassed human SotA in all 3 targeted ML tasks]"
6 posts
#3
R, RL, Emp
: ""1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities", Wang et al. 2025"
4 posts
#4
Code
: "Aristotle SMASHES Putnam By Solving & Formally Verifying 10/12 Problems. We Are Entering A New Dawn For AI And Mathematics. Slowly…..Then All At Once!!"
2 posts
#5
OP, D
: ""LLMs as Giant Lookup-Tables of Shallow Circuits", Niplav (metaphor for how to think of NN learning/capabilities)"
2 posts
#6
N, A, Econ
: ""Anthropic raises $65B in Series H funding at $965B post-money valuation""
2 posts
#7
R, Theory, Emp
: ""Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law", Kunstner & Bach 2025"
2 posts
#8
Emp, M-L
: "PostTrainBench: Measuring how well AI agents can post-train [small] language models"
2 posts
#9
X
: "Elon Musk pushes out more xAI founders as AI coding effort falters"
1 post
#10
OA
: "Terence Tao's Thoughts On GPT-5.2 Fully Automously Solving Erdos Problem #728"
1 post
Member Growth in r/mlscaling
Yearly
+5k members(33.6%)
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Last updated: June 27, 2026