Add TRL GRPO Reasoning with Advanced Reward notebook #319
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qgallouedec
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Jul 26, 2025
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Thanks for the addition! 😄
We already have a pretty similar example "Post training an LLM for reasoning with GRPO in TRL".
The idea of the repo is to have end-to-end recipes with extended explanations, so I'd suggest:
- Extending the explanations throughout the recipe of the example.
- Link the previous example and make a clear distinction between them, explaining it at the beginning. Otherwise, it could lead to confusion for a possible reader looking for an example of GRPO.
The recipes can be opened in Colab and maybe run, so I'd also be nice to keep that in mind. For example when doing os.environ["CUDA_VISIBLE_DEVICES"] = "1" since in Colab there is only 1 GPU.
11 tasks
This notebook demonstrates how to use TRL (Transformers Reinforcement Learning) with GRPO (Group Relative Policy Optimization) for reasoning tasks with advanced reward mechanisms. - Added notebook with proper lowercase filename - Updated _toctree.yml and index.md - Added proper author attribution - Cleaned non-informative outputs Contributed by: Behrooz Azarkhalili
- Remove torch and accelerate from installation (dependencies of TRL) - Remove pad token check (handled automatically) - Restore num_generations to default value (8) - Remove remove_unused_columns parameter (false by default) - Remove processing_class parameter (loaded automatically)
…O recipe - Add direct link to existing HuggingFace GRPO cookbook example - Fix CUDA device setting for Colab compatibility (auto-detect instead of hardcoded) - Add comprehensive explanations throughout all recipe sections - Enhance with detailed comparison table showing differences from basic example - Improve GPU setup with memory information and fallback instructions - Add detailed LoRA configuration explanations and parameter analysis - Expand dataset preparation with GSM8K background and format details - Detail multi-reward system design for mathematical reasoning approach - Optimize training configuration with Colab-specific memory settings - Enhance testing and evaluation with detailed response analysis - Make notebook fully end-to-end recipe focused for cookbook standards - Address all reviewer feedback comprehensively for cookbook contribution
…anup Major improvements to GRPO mathematical reasoning notebook: Content Organization: - Streamlined introduction removing verbose explanations - Simplified installation and setup sections with clear instructions - Updated all markdown cells to be concise and action-oriented - Improved inline comments to explain technical decisions and "why" behind code Technical Enhancements: - Added trackio experiment tracking with comprehensive configuration - Implemented timestamp-based unique run naming for session separation - Enhanced logging configuration to suppress verbose HTTP request logs - Optimized training parameters for mathematical reasoning tasks - Improved model evaluation section with structured output validation Code Quality: - Clean, consistent formatting across all 38 cells - Removed decorative print statements and emojis from evaluation section - Added proper error handling and documentation - Streamlined resource management and GPU memory optimization Resource Management: - Added remove_trackio_project() function for database cleanup - Comprehensive cleanup section with storage management - Warning comments about permanent data deletion - Proper resource freeing with GPU cache clearing Testing and Validation: - Enhanced model testing with optimized generation parameters - Improved format compliance checking with detailed validation - Better answer accuracy verification with extraction methods - Comprehensive response analysis and debugging output This represents the final polished version ready for production use, incorporating all previous feedback and implementing best practices for educational content, technical accuracy, and resource management.
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Integrates trackio experiment tracking to capture and visualize GRPO training metrics including reward scores, KL divergence, policy gradients, and completion statistics. Also removes unnecessary Open In Colab button as it's automatically added by the platform.
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Resolved. It came back during rebase :) |
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
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Advanced GRPO Fine-tuning for Mathematical Reasoning with Multi-Reward Training
This PR adds a comprehensive notebook demonstrating advanced GRPO (Group Relative Policy Optimization) for mathematical reasoning tasks using a sophisticated multi-reward training system.
Key Features
🧠 Advanced Training Approach
⚡ Memory-Efficient Implementation
📊 Comprehensive Experiment Tracking
🎯 Production-Ready Features
Technical Implementation
The notebook demonstrates:
Usage
The notebook is self-contained and includes:
This implementation serves as a practical guide for researchers and practitioners working on mathematical reasoning with RLHF techniques.