#mapreducealgorithms zoekresultaten
Wish to build scaling laws for RL but not sure how to scale? Or what scales? Or would RL even scale predictably? We introduce: The Art of Scaling Reinforcement Learning Compute for LLMs
Meet MapAnything – a transformer that directly regresses factored metric 3D scene geometry (from images, calibration, poses, or depth) in an end-to-end way. No pipelines, no extra stages. Just 3D geometry & cameras, straight from any type of input, delivering new state-of-the-art…
The first fantastic paper on scaling RL with LLMs just dropped. I strongly recommend taking a look and will be sharing more thoughts on the blog soon. The Art of Scaling Reinforcement Learning Compute for LLMs Khatri & Madaan et al.
the tools to make research like a pro (better than 98%) @glassnode: on-chain market insights @dappradar: web3 projects aggregator @defillama: largest data aggregator for DeFi @arkham: on-chain analytics platform @dune: crypto's data hub (dashboards) @artemis: on-chain…
Researchers from the Robotics Institute + Meta Reality Labs have built a model that reconstructs images, camera data or depth scans into 3D maps within a unified system! MapAnything captures both small details and large spaces with high precision 🌎🗺️📌 bit.ly/4haR7Qm
1/8 Second Order Optimizers like SOAP and Muon have shown impressive performance on LLM optimization. But are we fully utilizing the potential of second order information? New work: we show that a full second order optimizer is much better than existing optimizers in terms of…
A time-complexity cheat sheet of 10 ML algorithms: What's the inference time-complexity of KMeans?
Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices.…
Banger paper from Meta and collaborators. This paper is one of the best deep dives yet on how reinforcement learning (RL) actually scales for LLMs. The team ran over 400,000 GPU hours of experiments to find a predictable scaling pattern and a stable recipe (ScaleRL) that…
Dropped a GitHub repo full of unique pattern algorithms for DSA & CP, with time complexities! Includes: KMP, Kadane, DSU, & more, perfect for interview prep or brushing up your DSA skills. PRs, suggestions & contributions welcome! ⭐ github.com/Aarushi-bhatia…
There is so much noise in the LLM RL space, so we sat down and ran everything at scale (so you dont have to 😜) and presenting to you “The Art of Scaling RL” Give this a read before starting your next RL run. Led by amazing @Devvrit_Khatri @lovish
Wish to build scaling laws for RL but not sure how to scale? Or what scales? Or would RL even scale predictably? We introduce: The Art of Scaling Reinforcement Learning Compute for LLMs
Cool new repo - a complete Mamba implementation in MLX! Includes inference and training. Mamba is a state space model so fixed memory even for long generations. Code: github.com/alxndrTL/mamba… Generating with the 2.8B model (in 32-bit precision) on an M2 Ultra:
Happy to release an MLX implementation of Mamba 🐍, now part of mamba.py Run inference of pretrained models and train models right on your Mac !
.@Oracle and AMD are expanding AI performance and scalability with the general availability of OCI Compute featuring AMD Instinct MI355X GPUs. Built on the AMD CDNA 4 architecture, the MI355X provides nearly 3x the compute power and 50% more high-bandwidth memory, giving…
Holy shit... Meta just cracked the art of scaling RL for LLMs. For the first time ever, they showed that "reinforcement learning follows predictable scaling laws" just like pretraining. Their new framework, 'ScaleRL', fits a sigmoid compute-performance curve that can forecast…
⬛🟧⬛ ReflecMino 2025/10/17 🟧⬜🟦 yavu.github.io/yv_reflecmino/ ⬛🟦⬛ Solved in 00:34
New Special Issue: Big Data Algorithmics mdpi.com/journal/algori… #ComputationalModels #MapReduceAlgorithms #StreamingAlgorithms #ImpossibilityResults #algorithms #openaccess
#Hadoop is an open-source framework that allows to store and process big data, #MapReducealgorithms and #DistributedFileSystem environment across clusters of computers using simple programming models. bit.ly/2E3ngcK @tutorialspoint
New Special Issue: Big Data Algorithmics mdpi.com/journal/algori… #ComputationalModels #MapReduceAlgorithms #StreamingAlgorithms #ImpossibilityResults #algorithms #openaccess
#Hadoop is an open-source framework that allows to store and process big data, #MapReducealgorithms and #DistributedFileSystem environment across clusters of computers using simple programming models. bit.ly/2E3ngcK @tutorialspoint
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