Papers

Filtered to distributed training · clear filter

Browse by term

continual learning 64reinforcement learning 34large language models 17benchmarking 12vision-language models 10generative models 8language models 8video generation 7multimodal models 6natural language processing 6robotics 6world models 6benchmarks 5diffusion models 5on-policy distillation 5policy optimization 5scalability 5self-distillation 5vision-language-action models 5autoregressive models 4computer vision 4diffusion transformers 4LLMs 4multimodal large language models 4verifiable rewards 4attention mechanisms 3embodied intelligence 3image editing 3long-term memory 3multimodal learning 3

Matching papers

Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning

28 upvotes · 22 JUL 2026 · Jian Hu, Huiying Li, Hao Zhang et al.

This paper introduces Molt, a lightweight PyTorch framework for agentic reinforcement learning that aims to simplify the development process by reducing the overhead of algorithm modifications and framework changes. Practitioners might care about Molt because it can help them build and train reinforcement learning models more efficiently.

reinforcement learningagentic reinforcement learningPyTorchscalable frameworksdistributed training

FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation

8 upvotes · 17 JUL 2026 · Hao Liu, Chenghuan Huang, Ye Huang et al.

This paper develops a more efficient way to generate high-quality videos by balancing the workload across multiple GPUs during training, which can improve the performance of video generation models like those used in FVAttn. Practitioners in video generation and deep learning might care about this research because it can lead to faster and more efficient video generation models.

sparse attentionvideo generationdistributed trainingGPU parallelismload balancing