hermes-agent/skills/mlops/grpo-rl-training

GRPO/RL Training Skill

Expert-level guidance for Group Relative Policy Optimization with TRL

📁 Skill Structure

grpo-rl-training/
├── SKILL.md                              # Main skill documentation (READ THIS FIRST)
├── README.md                             # This file
├── templates/
│   └── basic_grpo_training.py            # Production-ready training template
└── examples/
    └── reward_functions_library.py       # 20+ reward function examples

🚀 Quick Start

1. Read SKILL.md - Comprehensive guide with all concepts and patterns
2. Copy templates/basic_grpo_training.py - Start with working code
3. Browse examples/reward_functions_library.py - Pick reward functions for your task
4. Modify for your use case - Adapt dataset, rewards, and config

💡 What's Inside

SKILL.md (Main Documentation)

• Core GRPO concepts and algorithm fundamentals
• Complete implementation workflow (dataset → rewards → training → deployment)
• 10+ reward function examples with code
• Hyperparameter tuning guide
• Training insights (loss behavior, metrics, debugging)
• Troubleshooting guide
• Production best practices

Templates

• basic_grpo_training.py: Minimal, production-ready training script
  • Uses Qwen 2.5 1.5B Instruct
  • 3 reward functions (format + correctness)
  • LoRA for efficient training
  • Fully documented and ready to run

Examples

• reward_functions_library.py: 20+ battle-tested reward functions
  • Correctness rewards (exact match, fuzzy match, numeric, code execution)
  • Format rewards (XML, JSON, strict/soft)
  • Length rewards (ideal length, min/max)
  • Style rewards (reasoning quality, citations, repetition penalty)
  • Combined rewards (multi-objective optimization)
  • Preset collections for common tasks

📖 Usage for Agents

When this skill is loaded in your agent's context:

1. Always read SKILL.md first before implementing
2. Start simple - Use length-based reward to validate setup
3. Build incrementally - Add one reward function at a time
4. Reference examples - Copy patterns from reward_functions_library.py
5. Monitor training - Watch reward metrics (not loss!)

🎯 Common Use Cases

Task Type	Recommended Rewards	Template
Math reasoning	MATH_REASONING_REWARDS preset	basic_grpo_training.py
Code generation	CODE_GENERATION_REWARDS preset	Modify dataset in template
Summarization	SUMMARIZATION_REWARDS preset	Adjust prompts + rewards
Q&A	QA_REWARDS preset	Use fuzzy match + citations

⚠️ Critical Reminders

• Loss goes UP during training - This is normal (it's KL divergence)
• Use 3-5 reward functions - Single rewards often fail
• Test rewards before training - Debug each function independently
• Monitor reward_std - Should stay > 0.1 (avoid mode collapse)
• Start with num_generations=4-8 - Scale up if GPU allows

🔗 External Resources

• TRL Documentation
• DeepSeek R1 Paper
• Open R1 Implementation
• Unsloth (2-3x faster)

📝 Version

v1.0.0 - Initial release (January 2025)

👨‍💻 Maintained By

Orchestra Research For questions or improvements, see https://orchestra.com

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License: MIT Last Updated: January 2025