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Scaling RL to Long Videos
Paper • 2507.07966 • Published • 161 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 320 -
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning
Paper • 2507.14111 • Published • 24 -
MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Paper • 2507.21183 • Published • 15
laner ten
that113
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The Ultra-Scale Playbook
🌌3.92kThe ultimate guide to training LLM on large GPU Clusters
-
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Paper • 2504.02587 • Published • 32 -
RLHF Workflow: From Reward Modeling to Online RLHF
Paper • 2405.07863 • Published • 71 -
microsoft/Magma-8B
Robotics • 9B • Updated • 540 • 417
-
Scaling RL to Long Videos
Paper • 2507.07966 • Published • 161 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 320 -
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning
Paper • 2507.14111 • Published • 24 -
MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Paper • 2507.21183 • Published • 15
-
The Ultra-Scale Playbook
🌌3.92kThe ultimate guide to training LLM on large GPU Clusters
-
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Paper • 2504.02587 • Published • 32 -
RLHF Workflow: From Reward Modeling to Online RLHF
Paper • 2405.07863 • Published • 71 -
microsoft/Magma-8B
Robotics • 9B • Updated • 540 • 417
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