Free interactive course teaching foundational and advanced Reinforcement Learning: Markov Decision Processes, Policy Gradients, Actor-Critic methods, and Proximal Policy Optimization (PPO).
To deeply understand RLHF (PPO) and modern reasoning reinforcement learning (RLVR / GRPO) in Modules 7.3 and 7.4, you need solid intuition for policy gradients, value functions, advantage estimation, and clipped objective ratios. The Hugging Face Deep RL Course teaches these foundations from scratch with clear theory chapters and PyTorch implementations.
Course status is tracked independently from roadmap topic checkboxes so you can mark external courses without altering your topic mastery percentage.
Phase 7 RL Theory & PPO Deep Dive (Modules 7.3 & 7.4)
100% free interactive web units and Colab notebooks on Hugging Face.
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
Focus on Unit 1 (Introduction to RL), Unit 4 (Policy Gradient), Unit 6 (Actor-Critic), and Unit 8 (Proximal Policy Optimization — PPO) alongside Modules 7.3 and 7.4.
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.
Official documentation and technical guides for TRL (Transformer Reinforcement Learning), covering `SFTTrainer`, `RewardTrainer`, `PPOTrainer`, `DPOTrainer`, and `GRPOTrainer`.
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.