post-training-trl-fine-tuning

Topics in the source

• TRL - Transformer Reinforcement Learning
• Quick start
• Common workflows
• Workflow 1: Full RLHF pipeline (SFT → Reward Model → PPO)
• Workflow 2: Simple preference alignment with DPO
• Workflow 3: Memory-efficient online RL with GRPO
• When to use vs alternatives
• Common issues

TRL provides post-training methods for aligning language models with human preferences. Installation: Supervised Fine-Tuning (instruction tuning): DPO (align with preferences): Complete pipeline from base model to human-aligned model. Copy this checklist: Step 1: Supervised fine-tuning Train base model on instruction-following data: Step 2: Train reward model Train model to predict human preferences: Step 3: PPO reinforcement learning Optimize policy using reward model: Step 4: Evaluate Align model with preferences without reward model. Copy this checklist: Step 1: Prepare preference dataset Dataset format: Load dataset: Step 2: Configure DPO Step 3: Train with DPOTrainer CLI alternative: Train with reinforcement learning using minimal memory. Copy this checklist: Step 1: Define reward function Or use a reward model: Step 2: Configure GRPO Step 3: Train with GRPOTrainer CLI: Use TRL when: Need to align model with human preferences Have preference data (chosen/rejected pairs) Want to use reinforcement learning (PPO, GRPO) Need reward model training Doing RLHF (full pipeline) Method selection: SFT: Have prompt-completion pairs, want basic instruction following DPO: Have preferences,

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