Robuta

https://huggingface.co/papers/2502.12947
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knowledge distillationpapereveryexpertmatters
https://openreview.net/forum?id=thCvCNpSkd&referrer=%5Bthe%20profile%20of%20Pieter%20Simoens%5D(%2Fprofile%3Fid%3D~Pieter_Simoens1)
Recent trends in Reinforcement Learning (RL) highlight the need for agents to learn from reward-free interactions and alternative supervision signals, such as...
mixtureautoencoderexpertsguidanceusing
https://arxiv.org/html/2403.17749v2
multi taskdensepredictionviamixture
https://www.deeplearning.ai/the-batch/issue-315/
Oct 14, 2025 - The Batch AI News and Insights: On Saturday at the Buildathon [http://buildathon.ai] hosted by AI Fund and DeepLearning.AI, over 100 developers...
chinaquestionsnvidiamodelsmemorize
https://openreview.net/forum?id=ZEC0oBtzhN&referrer=%5Bthe%20profile%20of%20Joel%20Hestness%5D(%2Fprofile%3Fid%3D~Joel_Hestness2)
Mixture of Experts (MoE) architectures offer a promising avenue for scaling neural networks by facilitating parameter-efficient model expansion while...
mixture of expertstowardsbetterroutingmethods
https://substack.recursal.ai/p/flock-of-finches-rwkv-6-mixture-of
The largest RWKV MoE model yet!
flockfinchesmixtureexperts
https://arxiv.org/html/2412.07067v6
moecapbenchmarkingcostaccuracy
https://arxiv.org/abs/2410.10896v2
Abstract page for arXiv paper 2410.10896v2: AT-MoE: Adaptive Task-planning Mixture of Experts via LoRA Approach
mixture of expertsmoeadaptivetaskplanning
https://openreview.net/forum?id=TvSQpR7VgL&referrer=%5Bthe%20profile%20of%20Qiyue%20Yin%5D(%2Fprofile%3Fid%3D~Qiyue_Yin1)
Despite the dramatic success in image generation, Generative Adversarial Networks (GANs) still face great challenges in synthesizing sequences of discrete...
improvedtrainingmixtureexpertslanguage
https://blog.dragonscale.ai/mixture-of-experts/
Discover how the Mixture of Experts approach revolutionizes Large Language Models. Learn about its efficiency in language processing, architectural...
mixture of expertslarge language modelsaienhancing
https://huggingface.co/papers/1701.06538
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