nvidia-cosmos/cosmos-rl
Aug 2025 – Nov 2025
3 Months active
Languages Used
PythonTOML
Technical Skills
Configuration ManagementDistributed TrainingLLM IntegrationReinforcement LearningBug FixCode Refactoring
heslami
PROFILE
Heslami
Worked on the nvidia-cosmos/cosmos-rl repository to deliver experimental support for Deepseek V3 GRPO, integrating the model into the reinforcement learning framework and updating data packing mechanisms for new model types. Focused on code refactoring and configuration management by removing legacy implementations, cleaning up unused logic, and updating documentation to streamline onboarding and reduce runtime risk. Led an architectural refactor to decouple tokenizer handling within DataPacker, simplifying data flow and enabling future support for non-tokenizer modalities. Utilized Python and TOML for model configuration, data processing, and distributed training, emphasizing maintainability and generalizability across evolving machine learning workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
3,349
Activity Months3
Your Network
1652 peopleSame Organization
@nvidia.com1629
Work History
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 focused on architectural refactor in cosmos-rl to generalize data processing and reduce cross-module tokenizer dependencies. Centralized tokenizer handling within DataPacker, enabling future tokenizer-free data reading paths and broader applicability to non-tokenizer modalities, while preserving existing training/inference pipelines.
1 Commits • 1 Features
Nov 1, 2025November 2025 focused on architectural refactor in cosmos-rl to generalize data processing and reduce cross-module tokenizer dependencies. Centralized tokenizer handling within DataPacker, enabling future tokenizer-free data reading paths and broader applicability to non-tokenizer modalities, while preserving existing training/inference pipelines.
November 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025In September 2025, delivered targeted cleanup and stability enhancements for Deepseek-V3 in nvidia-cosmos/cosmos-rl. Removed the legacy Deepseek-V3 implementation and related configuration to reduce confusion after the new Deepseek V3 and R1 support. Fixed a GRPO bug in Deepseek-V3 when using EP by removing it from the MoE rollout and cleaned up unused logic in the weight mapper to improve compatibility and prevent assertion errors. Updated documentation accordingly. These changes reduce runtime risk, align with the latest Deepseek-V3/R1 features, and simplify onboarding and usage.
September 2025
2 Commits • 1 Features
Sep 1, 2025In September 2025, delivered targeted cleanup and stability enhancements for Deepseek-V3 in nvidia-cosmos/cosmos-rl. Removed the legacy Deepseek-V3 implementation and related configuration to reduce confusion after the new Deepseek V3 and R1 support. Fixed a GRPO bug in Deepseek-V3 when using EP by removing it from the MoE rollout and cleaned up unused logic in the weight mapper to improve compatibility and prevent assertion errors. Updated documentation accordingly. These changes reduce runtime risk, align with the latest Deepseek-V3/R1 features, and simplify onboarding and usage.
August 2025
1 Commits • 1 Features
Aug 1, 2025Month: 2025-08 – Key accomplishments include delivering Deepseek V3 GRPO experimental support in nvidia-cosmos/cosmos-rl with a new configuration, integrating the model into the reinforcement learning framework, and updating data packing and rollout mechanisms to accommodate the Deepseek V3 GRPO model type. No major bugs fixed this period. This work enhances RL experimentation capabilities and accelerates evaluation of Deepseek V3 within the Cosmos RL pipeline.
1 Commits • 1 Features
Aug 1, 2025Month: 2025-08 – Key accomplishments include delivering Deepseek V3 GRPO experimental support in nvidia-cosmos/cosmos-rl with a new configuration, integrating the model into the reinforcement learning framework, and updating data packing and rollout mechanisms to accommodate the Deepseek V3 GRPO model type. No major bugs fixed this period. This work enhances RL experimentation capabilities and accelerates evaluation of Deepseek V3 within the Cosmos RL pipeline.
August 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture85.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
PythonTOML
Technical Skills
Bug FixCode RefactoringConfiguration ManagementDistributed TrainingDocumentation UpdateLLM IntegrationModel ConfigurationPythonRefactoringReinforcement Learningdata processingmachine learning
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