nvidia-cosmos/cosmos-rl
Nov 2025 – Mar 2026
4 Months active
Languages Used
Python
Technical Skills
Deep LearningDistributed SystemsMachine LearningModel OptimizationPyTorchQuantization
yufanhuangNV
PROFILE
Yufanhuangnv
Worked on the nvidia-cosmos/cosmos-rl repository to advance large-scale model efficiency and deployment readiness. Developed DeepEP support for Qwen3-MoE models and implemented FP4 dynamic quantization for linear layers, leveraging PyTorch and distributed systems to improve policy training throughput and stability. Addressed tensor export compatibility, enabling seamless inference with Hugging Face transformers and vLLM. Enhanced video processing by integrating WAN2.2 VAE support in the reward service, adding flexible configuration and robust testing. Delivered Flash Attention FA3 support with adaptable return types, strengthening attention mechanisms for experimentation and debugging. Work demonstrated depth in deep learning, quantization, and model optimization.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
4
Lines of code
4,330
Activity Months4
Your Network
1647 peopleSame Organization
@nvidia.com1624
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for nvidia-cosmos/cosmos-rl: Delivered Flash Attention FA3 support with flexible return types for the flash_attn_varlen_func, enabling both return types based on return_attn_probs flag and enhancing the attention mechanism's versatility. This work includes a targeted bug fix to align FA3 behavior within flash_attn_varlen_func. Result: greater flexibility for attention outputs, improved debugging capabilities, and stronger foundation for FA3-enabled experiments and deployment readiness.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for nvidia-cosmos/cosmos-rl: Delivered Flash Attention FA3 support with flexible return types for the flash_attn_varlen_func, enabling both return types based on return_attn_probs flag and enhancing the attention mechanism's versatility. This work includes a targeted bug fix to align FA3 behavior within flash_attn_varlen_func. Result: greater flexibility for attention outputs, improved debugging capabilities, and stronger foundation for FA3-enabled experiments and deployment readiness.
March 2026
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for nvidia-cosmos/cosmos-rl focused on delivering WAN2.2 VAE support in the reward service to enhance video decoding capabilities. The work emphasizes business value from improved video processing, deployment flexibility, and robust testing.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for nvidia-cosmos/cosmos-rl focused on delivering WAN2.2 VAE support in the reward service to enhance video decoding capabilities. The work emphasizes business value from improved video processing, deployment flexibility, and robust testing.
January 2026
1 Commits
Jan 1, 2026Concise monthly summary for 2026-01 focused on achieving interoperability and deployment readiness for the cosmos-rl module. Delivered a critical tensor export compatibility fix ensuring seamless inference with Hugging Face transformers and vLLM after enabling DeepEP, aligning with model governance and deployment needs.
1 Commits
Jan 1, 2026Concise monthly summary for 2026-01 focused on achieving interoperability and deployment readiness for the cosmos-rl module. Delivered a critical tensor export compatibility fix ensuring seamless inference with Hugging Face transformers and vLLM after enabling DeepEP, aligning with model governance and deployment needs.
January 2026
November 2025
2 Commits • 2 Features
Nov 1, 2025Month 2025-11 focused on advancing model efficiency and training throughput in Nvidia Cosmos RL. Delivered DeepEP support for Qwen3-MoE models with measurable performance gains and resolved critical stability issues. Implemented FP4 dynamic quantization for linear layers to boost policy training efficiency, enabling substantial quantization-enabled throughput improvements while integrating NVFP4 quantizer and transformer engine GEMM. Key work spans nvidia-cosmos/cosmos-rl with notable contributions to large MoE models and policy training pipelines.
November 2025
2 Commits • 2 Features
Nov 1, 2025Month 2025-11 focused on advancing model efficiency and training throughput in Nvidia Cosmos RL. Delivered DeepEP support for Qwen3-MoE models with measurable performance gains and resolved critical stability issues. Implemented FP4 dynamic quantization for linear layers to boost policy training efficiency, enabling substantial quantization-enabled throughput improvements while integrating NVFP4 quantizer and transformer engine GEMM. Key work spans nvidia-cosmos/cosmos-rl with notable contributions to large MoE models and policy training pipelines.
Activity
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Quality Metrics
Correctness88.0%
Maintainability80.0%
Architecture84.0%
Performance80.0%
AI Usage48.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningDistributed SystemsMachine LearningModel OptimizationPyTorchPythonQuantizationVideo Processing
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