grpo-rl-training

Expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. This skill provides battle-tested patterns, critical insights, and production-ready workflows for fine-tuning language models with custom reward functions. Use GRPO training when you need to: Do NOT use GRPO for: Key Mechanism: Critical Difference from PPO: Mathematical Intuition: Golden Rules: Reward Function Types: Critical Requirements: Example Structure: Pro Tips: Template Structure: Example 1: Correctness Reward (Math/Coding) Example 2: Format Reward (Structured Output) Example 3: Incremental Format Reward (Partial Credit) Critical Insight: Combine 3-5 reward functions for robust training. Order matters less than diversity of signals. Memory-Optimized Config (Small GPU) High-Performance Config (Large GPU) Critical Hyperparameters: Standard Setup (Transformers) Unsloth Setup (2-3x Faster) Key metrics to watch: Healthy Training Pattern: Warning Signs: For complex tasks, train in stages: Before Training: During Training: After Training: Official Documentation: Example Repositories: Recommended Reading: When this skill is loaded: Critical Reminders: This skill is designed for expert-level implementation . Beginners should start with supervised fine-tuning before attempting GRPO. - Enforce specific output formats (e.g., XML tags, JSON,...