post-training-grpo-rl-training

Topics in the source

• GRPO/RL Training with TRL
• When to Use This Skill
• Core Concepts
• 1. GRPO Algorithm Fundamentals
• 2. Reward Function Design Philosophy
• Implementation Workflow
• Step 1: Dataset Preparation
• Step 2: Reward Function Implementation

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: Enforce specific output formats (e.g., XML tags, JSON, structured reasoning) Teach verifiable tasks with objective correctness metrics (math, coding, fact-checking) Improve reasoning capabilities by rewarding chain-of-thought patterns Align models to domain-specific behaviors without labeled preference data Optimize for multiple objectives simultaneously (format + correctness + style) Do NOT use GRPO for: Simple supervised fine-tuning tasks (use SFT instead) Tasks without clear reward signals When you already have high-quality preference pairs (use DPO/PPO instead) -- Key Mechanism: Generates multiple completions for each prompt (group size: 4-16) Compares completions within each group using reward functions Updates policy to favor higher-rewarded responses relative to the group Critical Difference from PPO: No separate reward model needed More sample-eff

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