What is RLHF?
RLHF stands for reinforcement learning from human feedback. It is a post-training method that adjusts the behavior of a pretrained language model using human preference judgments instead of just more text data. Humans compare model outputs, indicate which response is better, and that signal gets turned into a training objective.
The RLHF pipeline has three stages. First, supervised fine-tuning (SFT) teaches the model a task format. Second, a reward model learns to score outputs based on how humans ranked them. Third, reinforcement learning, typically using proximal policy optimization (PPO), improves the model's responses while keeping it from drifting too far from the fine-tuned version. This is the process behind ChatGPT, Claude, and most other conversational AI systems people use daily.
Why does human judgment matter in RLHF?
Pretraining builds a model that's very good at one thing—predicting the next token in a sequence—but that's not helpful or safe. A model trained purely on next-token prediction will happily complete a harmful prompt if the pattern matches its training data. It has no concept of "you shouldn't answer that." It has a concept of "here's what statistically follows."
This gap isn't a bug; it's structural. The objective during pretraining is prediction accuracy, rather than alignment with human values. Scaling up the data doesn't fix it either. Stiennon et al. (2020) showed that human preference data produced better summaries than models trained on larger datasets alone. Ouyang et al. (2022), in the InstructGPT paper, demonstrated the same at scale: a 1.3B-parameter model with RLHF training outperformed a 175B-parameter model without it on human preference evaluations.
More data makes a model more capable, not more aligned. RLHF exists because a different kind of signal, human preference, was needed to close this gap. Reinforcement learning applied to large language models (LLMs) is the mechanism that injects the signal after pretraining.
How does RLHF work? (The 3 Stages)
The RLHF pipeline is a sequence of three stages, and each one depends on the output of the stage before it. Skip the first SFT step, and the reward model trains on incoherent outputs. Train a weak reward model, and the RL stage optimizes for the wrong thing. The stages build on each other, which is why "we did RLHF" means less than "here's how we did each stage."
Here's how it works from start to finish.
Stage 1: Supervised fine-tuning (SFT): teaching the model your task format
SFT is the bridge between a general-purpose pretrained model and one that can follow instructions. The base model is fine-tuned on a curated dataset of prompt-response pairs, where the responses are written or vetted by humans. The model learns the format: given this kind of input, produce this kind of output.
This isn't RLHF yet. It's standard supervised learning. This distinction matters because SFT alone yields a model that can produce reasonably formatted answers but has no mechanism to distinguish a good answer from a mediocre one. It knows what an answer looks like; it doesn't know what a better answer looks like.
The quality of the SFT dataset sets a ceiling on everything that follows. If the demonstration data is sloppy or generic, the RLHF fine-tuning stages inherit this weakness. Teams commonly underinvest here.
Stage 2: Reward model training: turning expert preferences into a score
This stage is vital since it’s where the RLHF reward model is built. The SFT model generates multiple responses to the same prompt, which are then ranked by human evaluators (“Response A is better than Response B”). These pairwise comparisons become training data for a separate model, the reward model, whose job is to assign a scalar score to any given output.
Christiano et al. (2017) established this pairwise preference approach in "Deep reinforcement learning from human preferences," and it remains the foundation of RLHF training. The reward model learns to approximate human judgment. Its accuracy depends directly on the consistency, expertise, and domain knowledge of the people doing the ranking.
This is also where quality variance hits hardest. If your evaluators disagree with each other or lack domain knowledge, the reward model learns a noisy signal. For example, a reward model trained on inconsistent medical evaluations will score outputs differently than one trained by physicians who agree on what a good answer looks like. Platforms such as Mercor supply the expert feedback that reward models depend on by connecting AI teams with credentialed domain specialists.
The reward model is the fulcrum of the whole RLHF pipeline. Everything downstream optimizes against it. Get it wrong, and you're optimizing in the wrong direction.
Stage 3: RL optimization (often PPO): improving answers without drifting off-distribution
With a reward model in hand, the final stage uses reinforcement learning to improve the SFT model's outputs. The most common algorithm is Proximal Policy Optimization (PPO). The model generates responses, the reward model scores them, and PPO updates the model's parameters to produce higher-scoring outputs.
The critical constraint is a Kullback-Leibler divergence penalty - a metric used to compare data distributions that keeps the RL-optimized model from straying too far from the SFT model. Without it, the model quickly learns to exploit the reward model, producing outputs that score high but read as strange or degenerate to humans.
The practical result is better instruction-following with guardrails. The model gets more helpful and is less likely to produce harmful content. It doesn't become truthful in any deep sense; it becomes better at producing outputs that look like human preferences during training. That distinction is important. RLHF aligns models with demonstrated preferences, not with ground truth.
RLHF use cases in real-world AI systems
RLHF's core job is aligning AI models with complex, subjective human values. This makes it well-suited to specific problem types, not all of them.
Conversational AI and virtual assistants: ChatGPT, Claude, and Gemini are the most visible applications. RLHF teaches these models to follow multistep instructions, handle context shifts without hallucinating, and respond with appropriate tone. The InstructGPT paper (Ouyang et al., 2022) documented exactly how this transformation works in practice.
Safety and content moderation: Refusal behavior is an RLHF artifact. So is bias mitigation: RLHF penalizes outputs that reproduce stereotypes embedded in pretraining data.
Domain-specific applications: In healthcare, law, and finance, RLHF trains models to express nuanced information with appropriate caveats rather than overconfident assertions, as well as to cite sources and respect regulatory constraints.
Code generation: Tools such as GitHub Copilot use RLHF to favor code that is readable, secure, and aligned with library best practices, not just syntactically valid.
Generative media: RLHF tunes image-generation models for aesthetic quality, text-to-speech systems for natural pacing and emotional tone, and game AI for coherent in-character behavior.
One important caveat: RLHF is most effective for subjective, nuanced tasks. SFT is usually more cost-effective for strictly rule-based or deterministic tasks.
Understanding how to train an AI model end-to-end clarifies the RLHF contribution. How frontier AI models are benchmarked on professional tasks reflects how well that stage was executed.
What are the limitations and trade-offs of RLHF?
RLHF has predictable failure modes, and knowing them changes how you evaluate the models that use it.
Reward hacking: The model learns to game the reward model rather than genuinely improve. Gao et al. (2022) quantified this in "Scaling Laws for Reward Model Overoptimization." Past a certain point, optimizing harder against the reward model makes outputs worse by human judgment, even as the reward score keeps climbing. The model finds patterns that the reward model rewards that humans wouldn't.
Preference misspecification: The reward model reflects the evaluators’ biases and blind spots. If evaluators prefer verbose responses, the model learns to be verbose. If they prefer confident-sounding answers over accurate hedging, the model learns to sound confident even when it shouldn't. The model mirrors the quality of its feedback.
Cost and scalability: RLHF training requires ongoing human evaluation, which is expensive and slower than self-supervised methods. Scaling it to cover more domains and languages multiplies the cost. This is a real constraint, not just an inconvenience.
Brittleness on-edge cases: RLHF-trained models perform well on the distribution of prompts they are evaluated on. This alignment breaks down when employing unusual domains, adversarial inputs, or novel question formats. The model reverts to base model behavior or produces outputs that the reward model never scored.
The takeaway: "RLHF-trained" is a signal to evaluate, not a trust badge.
RLHF vs. DPO vs. RLAIF vs. Constitutional AI
RLHF works, but it's expensive and complex. Several newer methods try to get similar results with lower overhead.
Direct preference optimization (DPO), introduced by Rafailov et al. (2023), skips the reward model entirely. Instead of training a separate model to score outputs and then running RL against it, DPO trains the language model directly on preference pairs. It reformulates the RLHF objective to optimize preferences in a single supervised step. This is simpler, cheaper, and avoids reward model instability. The trade-off is losing the ability to reuse the reward model for evaluation or for training other models.
Reinforcement learning from AI feedback (RLAIF) replaces human evaluators with another AI model. Instead of people ranking outputs, a separate LLM generates preference labels. This reduces costs and increases scale. Quality depends entirely on how good the AI evaluator is, creating a circular dependency by using a model's judgment to improve another model. RLAIF works well for clear-cut cases and poorly for ambiguous or high-stakes ones where it can't match human nuance.
Constitutional AI, from Bai et al. (2022) at Anthropic, combines both. The model critiques its own outputs against a set of written principles, then revises them. A mix of AI-generated and human-sourced feedback trains the preference model. It reduces the volume of human annotation needed without eliminating it.
None of these methods changes the need for high-quality human expertise in the initial instruction data, in the evaluation of hard cases, and in any domain where errors carry real consequences. The SFT foundation and edge-case evaluation still depend on specialists. Platforms for AI model training and expert feedback remain part of the pipeline regardless of which optimization method is used.
The importance of feedback quality in enhancing efficacy
The conversation around AI models tends to fixate on parameter counts and training compute. This framing misses the source of the variance in model quality. Two RLHF LLM systems with identical architectures and similar pretraining data can produce meaningfully different outputs based on who evaluated the training data and how carefully they did it.
Feedback quality is the variable. The people who compare outputs, rank responses, and flag failures are shaping the behavior of every model trained this way. This work necessitates domain knowledge, consistency, and the ability to articulate why one answer is better than another. It requires expertise.
If you're a domain expert interested in contributing to that process, learn how to get started in AI training work or apply to contribute to AI training projects on Mercor. If you're building AI systems and need expert feedback at the reward model stage, source the expert feedback your RLHF pipeline requires.
The models get better when the feedback gets better. This part hasn't changed, and it won't.
Frequently Asked Questions
What does RLHF stand for?+−
RLHF stands for Reinforcement Learning from Human Feedback. It’s a method used to train AI models using human opinions about what good output looks like. Instead of just learning from text data, the model learns from people who compare its answers and state which one is better. This process makes ChatGPT behave like a helpful assistant instead of a raw text generator.
How does RLHF work step-by-step?+−
First, SFT teaches the model to follow a prompt-response format. Second, human evaluators rank model outputs, and these rankings train a reward model that scores responses. Third, reinforcement learning (usually PPO) improves the model's outputs by optimizing them against the reward model's scores while keeping the model from drifting too far from its fine-tuned baseline.
What is the difference between RLHF and SFT?+−
SFT trains a model to produce outputs that match human-written examples. RLHF goes further: It trains the model to produce outputs that humans prefer over alternatives. SFT teaches format, while RLHF teaches quality. In practice, SFT is stage one of the RLHF pipeline, not a separate method.
Who provides the human feedback in RLHF?+−
Feedback is typically provided by trained evaluators with relevant domain knowledge. For general-purpose models, this might be a team of 30 to 50 people with clear rating guidelines. For specialized models (medical, legal, and coding), the evaluators need subject-matter expertise. The quality of these evaluators directly determines the quality of the resulting model.
Is RLHF still used in 2026?+−
Yes. Newer methods, such as DPO and RLAIF, have gained traction for specific use cases. However, RLHF remains the standard for training frontier models where output quality and safety matter most. Most major AI labs use RLHF or a close variant in their training pipelines.
What is PPO in RLHF?+−
PPO in RLHF stands for Proximal Policy Optimization. It is a reinforcement learning algorithm used to fine-tune AI models based on human feedback while keeping the model’s behavior stable during training. For example, after humans rank AI responses, PPO helps the model adjust its answers gradually so it improves without making large, harmful changes to its behavior.
What is DPO in RLHF? How are they different from each other?+−
RLHF trains a separate reward model and then uses reinforcement learning to optimize against it. DPO skips the reward model and trains the language model directly on preference data in a single supervised step. DPO is simpler and cheaper, but RLHF is more flexible and gives you a reusable reward model. The choice depends on your budget, data, and whether you need the reward model for other purposes.