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update Qwen3.5 grpo demo#124

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tastelikefeet merged 2 commits intomodelscope:mainfrom
hjh0119:qwen35-rl
Mar 22, 2026
Merged

update Qwen3.5 grpo demo#124
tastelikefeet merged 2 commits intomodelscope:mainfrom
hjh0119:qwen35-rl

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@hjh0119 hjh0119 commented Mar 22, 2026

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Summary of Changes

Hello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on enhancing the Qwen3.5 GRPO demo by optimizing its training parameters and LoRA setup. It also significantly improves the robustness of the GSM8K dataset processing and reward calculation by introducing support for a new answer format. Furthermore, the changes streamline the internal weight management and synchronization processes across different model types, leading to a more efficient and maintainable codebase.

Highlights

  • Qwen3.5 GRPO Demo Configuration: The Qwen3.5 GRPO demo has been updated with several configuration changes, including enabling Megatron by default, adjusting batch sizes, and introducing periodic model saving.
  • LoRA Configuration Refinement: The LoRA configuration for Qwen3.5 now explicitly targets a comprehensive list of linear projection modules, providing more precise control over adapter application.
  • GSM8K Dataset and Reward System Updates: The GSM8K dataset template's maximum length was reduced, and the preprocessor and reward system were enhanced to support a new '\boxed{}' answer format, improving flexibility in answer extraction and format validation.
  • Weight Synchronization and Loading Logic Improvements: The weight synchronization mechanisms in both Megatron and Transformers models were refined for better handling of LoRA weights and base model states. Concurrently, the vLLM worker extension's weight loading logic was simplified by expecting pre-normalized weight names from the sender.

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Code Review

This pull request updates the GRPO demo for the Qwen3.5 model. The changes include updating default configurations, specifying LoRA target modules, adding checkpoint saving, and modifying the GSM8K prompt and reward logic. A significant and beneficial change is the refactoring of the weight synchronization mechanism, which moves weight name normalization from the sampler to the model side, simplifying the overall logic. I have one minor suggestion to improve code maintainability.

@tastelikefeet tastelikefeet merged commit 196aa45 into modelscope:main Mar 22, 2026
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2 participants