Fine-tuning & RL for LLMs: Intro to Post-training
by DeepLearning.AI
Take a base LLM to production with SFT, RLHF, and RL — the full post-training pipeline, hands-on.
Overview
Fine-tuning & RL for LLMs: Intro to Post-training is a ~13h19m intermediate course from DeepLearning.AI built in partnership with AMD and taught by Sharon Zhou (VP of Engineering & AI at AMD), spanning 5 modules, 43 video lessons, and 11 graded, GPU-powered labs (free to audit; PRO membership for certificate). It teaches where post-training sits in the LLM lifecycle and how models gain reasoning, then walks through the core techniques: supervised fine-tuning (SFT), reward modeling, RLHF, and RL algorithms including PPO and GRPO, plus LoRA for parameter-efficient fine-tuning. Beyond training, it covers evaluation and error analysis — designing evals, detecting reward hacking, diagnosing failures, and red-teaming for robustness — as well as data preparation (building SFT/LoRA datasets, combining fine-tuning with RLHF, generating synthetic data, and balancing data against rewards). A final module takes learners from post-training to production with industry-style pipelines, go/no-go rules, and data feedback loops from logs. The methods are taught hardware-agnostic even though labs run on AMD GPUs.
At a Glance
- Topic
- Fine-Tuning
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~13h19m, self-paced (5 modules, 43 lessons, 11 labs)
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Where post-training fits in the LLM lifecycle and how models acquire reasoning
- ✓Supervised fine-tuning (SFT), reward modeling, and RLHF end to end
- ✓RL algorithms for LLMs: PPO and GRPO
- ✓LoRA for efficient, cost-aware fine-tuning
- ✓Designing evals, detecting reward hacking, and red-teaming for robustness
- ✓Preparing data — SFT/LoRA datasets, synthetic data, and balancing data vs. rewards
- ✓Shipping post-trained models with production pipelines and go/no-go rules
Highlights
- •Built with AMD; 11 hands-on GPU-powered labs across 13+ hours
- •Covers both fine-tuning AND reinforcement learning in one coherent pipeline
- •Strong focus on evaluation, reward hacking, and production readiness
Who It’s For
Best For
- ✓ML and AI engineers who want to post-train and align LLMs for real tasks
- ✓Developers moving beyond prompting into SFT, RLHF, and RL
- ✓Practitioners preparing fine-tuned models for production deployment
Prerequisites
- •Python and PyTorch basics
- •Familiarity with training/fine-tuning LLMs and transformer fundamentals
FAQ
What is Fine-tuning & RL for LLMs: Intro to Post-training?
A comprehensive DeepLearning.AI course, built with AMD and taught by Sharon Zhou, covering the modern LLM post-training pipeline end to end. It's for ML and AI engineers who want to align base models with fine-tuning and reinforcement learning and get them production-ready.
Is Fine-tuning & RL for LLMs: Intro to Post-training free?
Fine-tuning & RL for LLMs: Intro to Post-training offers free content, with paid options for certificates or premium features.
What level is Fine-tuning & RL for LLMs: Intro to Post-training for?
Fine-tuning & RL for LLMs: Intro to Post-training is aimed at a intermediate audience. Recommended background: Python and PyTorch basics, Familiarity with training/fine-tuning LLMs and transformer fundamentals.
How long does Fine-tuning & RL for LLMs: Intro to Post-training take?
Expect roughly ~13h19m, self-paced (5 modules, 43 lessons, 11 labs). Most learners work through it at their own pace.
What will I learn from Fine-tuning & RL for LLMs: Intro to Post-training?
You'll learn: Where post-training fits in the LLM lifecycle and how models acquire reasoning; Supervised fine-tuning (SFT), reward modeling, and RLHF end to end; RL algorithms for LLMs: PPO and GRPO; LoRA for efficient, cost-aware fine-tuning; Designing evals, detecting reward hacking, and red-teaming for robustness; Preparing data — SFT/LoRA datasets, synthetic data, and balancing data vs. rewards; Shipping post-trained models with production pipelines and go/no-go rules.