From Reward Modeling to KL Penalties: Every Stage of the RLHF Training Pipeline Explained
RLHF aligns language models through human preferences in three stages. Learn how reward models, PPO, and KL penalties interact to prevent reward hacking.
RLHF
Reinforcement Learning from Human Feedback (RLHF) is an alignment technique that fine-tunes large language models using human preference data instead of fixed labels.
Human annotators rank model outputs, training a reward model that guides optimization through algorithms like PPO or DPO. RLHF bridges the gap between a model's raw capabilities and the behaviors people actually want — helpful, harmless, and honest responses. Also known as: Reinforcement Learning from Human Feedback
What this topic covers
- Foundations — RLHF transforms raw language model capabilities into aligned behavior by letting human preferences — not handwritten rules — define what good output looks like.
- Implementation — The practical guides walk through reward model training, policy optimization pipelines, and the tooling decisions that determine whether your RLHF setup converges or collapses under reward hacking.
- What's changing — The RLHF landscape is shifting fast as alternatives like DPO and GRPO challenge the original approach.
- Risks & limits — Human annotators encode their own biases into reward models, and preference optimization can suppress minority viewpoints.
This topic is curated by our AI council — see how it works.
1
Understand the Fundamentals
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
Reward Hacking, Mode Collapse, and the Unsolved Technical Limits of RLHF Alignment
Reward hacking, mode collapse, and KL divergence failure — the three unsolved technical limits of RLHF alignment and why they resist simple fixes.
What Is RLHF and How Human Preferences Train Large Language Models to Follow Instructions
RLHF uses human preferences and reward models to train language models to follow instructions. Learn the three-stage PPO pipeline, why it works, and what replaced it.
2
Build with RLHF
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
How to Train a Language Model with RLHF Using OpenRLHF and TRL in 2026
Decompose, specify, and validate a full RLHF training pipeline with OpenRLHF and TRL in 2026. Covers SFT, reward modeling, PPO, and reward hacking defense.
3
What's Changing in 2026
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated March 2026
From ChatGPT's PPO to DeepSeek's GRPO: How RLHF Alternatives Reshaped Alignment Through 2026
Classical RLHF with PPO launched ChatGPT, but DPO and GRPO now dominate LLM alignment. See how reward-model-free methods reshaped AI training by 2026.
4
Risks and Considerations
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Annotator Exploitation, Preference Bias, and the Hidden Human Cost of RLHF Alignment
RLHF alignment relies on annotators paid poverty wages to label traumatic content. Explore the ethical cost of preference data and whose values get encoded into AI.