Official documentation and technical guides for TRL (Transformer Reinforcement Learning), covering `SFTTrainer`, `RewardTrainer`, `PPOTrainer`, `DPOTrainer`, and `GRPOTrainer`.
TRL is the industry-standard open-source library for post-training and aligning language models in PyTorch. Its documentation provides mathematical explanations, configuration guides, and runnable scripts for Supervised Fine-Tuning (`SFTTrainer`), Reward Modeling (`RewardTrainer`), Proximal Policy Optimization (`PPOTrainer`), Direct Preference Optimization (`DPOTrainer`), and Group Relative Policy Optimization (`GRPOTrainer`) for reasoning models.
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Phase 7 Core Technical Reference (Modules 7.1, 7.3 & 7.4)
100% free open-source documentation and example scripts.
Status: Verified (2026-11-09)
Review these foundations before starting so you get the most out of this material:
In Phase 7: Post-training & Alignment
Use as your primary API and mathematical reference during Modules 7.1, 7.3, and 7.4 when running SFT, DPO, PPO, and GRPO experiments.
Base vs. instruction-tuned models, chat templates, prompt-response loss masking, Supervised Fine-Tuning (SFT), and LIMA-style dataset quality.
Human preference datasets, Bradley-Terry reward modeling, RLHF with PPO, Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO).
Safe behavior, harmful output evaluation, calibrated refusals, bias mitigation, instruction hierarchy, red teaming, reward hacking, catastrophic forgetting, and calibration.
Practical hands-on course focused on post-training small and medium open-weight language models: chat templates, Supervised Fine-Tuning (SFT), LoRA/QLoRA parameter-efficient adaptation, preference alignment (DPO/ORPO), and evaluation.
Production recipes, hyperparameter configurations, and open-source training scripts for aligning language models (such as Zephyr and SmolLM-Instruct) using SFT, LoRA/QLoRA, and DPO.
Free interactive course teaching foundational and advanced Reinforcement Learning: Markov Decision Processes, Policy Gradients, Actor-Critic methods, and Proximal Policy Optimization (PPO).