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E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models
Paper • 2601.00423 • Published • 11 -
GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
Paper • 2601.05242 • Published • 235 -
FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning
Paper • 2601.18150 • Published • 11 -
DenseGRPO: From Sparse to Dense Reward for Flow Matching Model Alignment
Paper • 2601.20218 • Published • 16
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AI & ML interests
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Quantitation
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QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Paper • 2510.11696 • Published • 183 -
INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats
Paper • 2510.25602 • Published • 81 -
6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models
Paper • 2603.18742 • Published • 11 -
OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
Paper • 2607.02461 • Published • 41
RL
-
E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models
Paper • 2601.00423 • Published • 11 -
GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
Paper • 2601.05242 • Published • 235 -
FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning
Paper • 2601.18150 • Published • 11 -
DenseGRPO: From Sparse to Dense Reward for Flow Matching Model Alignment
Paper • 2601.20218 • Published • 16
Quantitation
-
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Paper • 2510.11696 • Published • 183 -
INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats
Paper • 2510.25602 • Published • 81 -
6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models
Paper • 2603.18742 • Published • 11 -
OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
Paper • 2607.02461 • Published • 41
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