Add TRL GRPO Reasoning with Advanced Reward notebook #319
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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.
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| - [TRL GRPO Reasoning with Advanced Reward](trl_grpo_reasoning_advanced_reward) | |||
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You can remove the last entry since we aim to have the last 5 here.
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Hi @sergiopaniego, I just added the notes you mentioned. I hope the extension and the differences between the two versions make sense now! 😊
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I'd suggest some possible ideas for improving this section
- I'd reduce this section since it contains too much text. Instead, you can distribute the ideas where they're more suitable. For example, explaining the rewards functions in the section where you introduce them.
- I'd remove the section and subsections and only keep the title. If you want to add some relevant information, you can consider using bold style.
- The comparison against the other example contains so problems. For example, in the other example we have two reward functions but here you say it's only one. I'd suggest reviewing that.
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Code blocks include a lot of code without explanation. I'd suggest dividing them into meaningful subblocks and add some explanation. Let's think about the target audience (learner) :)
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I'd be nice if we could reduce this blocks a little, since they contain a lot of details. Are all the hyperparams needed?
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We should explaining throughout the notebook the decisions made. Why do we need a callback (always think of a possible reader/learner)?
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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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Summary
This notebook demonstrates advanced GRPO (Group Relative Policy Optimization) fine-tuning for mathematical reasoning using a comprehensive multi-reward training system on the GSM8K dataset.
Key Features
Technical Improvements
Requirements Checklist
Recent Updates
This represents the final polished version ready for production use, incorporating all reviewer feedback and implementing best practices for educational content, technical accuracy, and resource management.
@merveenoyan @stevhliu @qgallouedec @sergiopaniego
Contributed by: Behrooz Azarkhalili