What Is RLHF? How AI Learns Human Preferences in 2026

A chatbot can spend six months in tuning, handle product inquiries, escalate tickets, and summarize policies, and still make for a nice clean demo that tells you almost nothing about how it'll perform in the wild. I don't take a demo's word for it, and neither should you. Testing it yourself, in your own environment, is the only way to know.

 Unfortunately, this bot could only ever generate responses that, while plausible, lacked the nuance that a human would bring to their support responses. For that reason, the bot was labeled as robotic, and as a direct consequence, user satisfaction went down. This was not caused by having a poor foundational model. The team had totally omitted an essential step in the development process, RLHF.

The example I provided is a prime example of what I see when large businesses decide to deploy their own AI. Companies are spending millions on prompt engineering and supervised fine-tuning. They then get outputs that they think are totally misaligned with what their users require, and wonder, 'what did we get wrong here?' RLHF is what addresses these concerns.

 

TL;DR: RLHF (Reinforcement Learning from Human Feedback) is the process that ensures large language models produce the outputs that users really want, and not just the outputs that are statistically most likely to be correct. It consists of three steps that are done in order: supervised fine-tuning, reward modeling, and reinforcement learning, with PPO being the learning algorithm. In their InstructGPT paper (arXiv:2203.02155), OpenAI claims a 1.3B parameter model trained with RLHF is more capable than a 175B model trained without RLHF.

 

What Is RLHF, Really?

At its most basic understanding, RLHF, Reinforcement Learning from Human Feedback, is a way to bring human preferences closer to the final stage of model optimization. In the case of language models, this means the model can learn to generate outputs that are more aligned to what real humans care about and less about what the training set was filled with.

Standard language model pretraining improves systems by optimizing for next-token completion. While RLHF does improve systems, there is limitation on the usefulness that can come from optimizing for next-token completion. Due to the nature of next-token completion, the models have no care whether the output is actually useful, honest, or safe. To address this, RLHF builds upon preference learning to score outputs based on human judgement. The scores then serve to augment the model's behavior using techniques employed in reinforcement learning.

The application of preference-based reinforcement learning to Atari Trajectories by Christiano and colleagues in 2017 was groundbreaking in a lot of respects. RLHF was then applied to the general-purpose large language models in OpenAI's InstructGPT (2022) and produced groundbreaking results that shifted an entire industry.

Why does AI even need human feedback?

Supervised learning is effective in situations with a clearly defined ground truth. For example, there are clear guidelines for translation, image classification, or sentiment analysis. These tasks define clear criteria and measurable loss.

Language generation, however, breaks that contract. Ask a model to generate language to complete an explanation in a manner that is both clear and helpful. What does loss look like for a completion that is clear and helpful? What does that look like in a loss function? There is no answer to this. This is a core limitation of human values.

This is a problem RLHF, in particular, was designed to solve. Rather than defining quality using mathematics, RLHF solves the problem by letting humans directly define quality through the comparison of outputs and the expression of preferences. The reward model is then tuned to infer these preferences during generation. The human presence is no longer required in the generation process.

Even advanced models without this system generate outputs that are factually plausible, but tend to be tone-deaf, unnecessarily verbose, or misleading. The model optimizes for statistically correct output. RLHF aims to optimize outputs for more satisfaction to users.

RLHF Pipeline: How it Functions, Step by Step

RLHF incorporates many steps in training. RLHF includes many steps in its pipeline, and each phase incorporates the previous.

Step One: Supervised Fine Tuning (SFT)

The first phase in the RLHF pipeline starts with a base pre-trained model. This model is then fine-tuned with a hand crafted selection of prompt-response pairs. This phase is often called supervised fine tuning, or instruction tuning.

The focus in this step is fairly narrow; the primary focus is to teach the model to respond to prompts and instructions in the expected output format. SFT is much more highly functional than the base model, and this improved functionality is the reason many groups stop improving the model at this step. I will explain why this is a mistake.

OpenAI, for InstructGPT, contracted 40 separate individuals via the platforms Upwork and Scale AI, to create the training data for SFT (OpenAI, arXiv:2203.02155). This number may seem relatively small, but it is of much more consequence than many people think.

Step Two: Reward Model Training

The next phase creates a separate neural network model, called the reward model, which is trained to predict human output preference. The following describes the steps for this.

Using the SFT model, you can generate a variety of responses for a specific prompt and present them in rank pairs to human annotators. These annotators are not tasked with scoring the answer, but rather determining which answer they consider superior. Compared to other methods, rankings vastly decrease label noise. This is because rankings have greater consistency than traditional scoring methods.

Ranked pairs form the basis for the reward model. With these pairs, the reward model estimates how a prompt-response pair will be ranked by a human by scoring it. After the reward model is trained, it will take the position of the human in the next iteration of the process.

The reward model has no generative capability. Its sole function is to score prompt-response pairs. This is critical during identification of alignment failures.

Stage 3: Policy Optimization via PPO

This is the phase where the SFT model is treated as a policy in a reinforcement learning framework. In this system, the SFT model and the reward model act in tandem to determine which responses will be scored and which policy parameters should be updated to maximize reward. The method used for this is the Proximal Policy Optimization (PPO) algorithm.

Most descriptions of this process ignore what is perhaps the most critical component. In this system, a KL divergence penalty is added to the reward signal. This penalty is an indicator of the distance the policy has shifted from the original SFT model and introduces a score for deviation. If this penalty is not introduced, the PPO will steer the outputs of the model towards maximizing reward model scores at the cost of coherence. If this penalty is active, the model is kept grounded.

The final reward signal looks like this: r = r_preference minus lambda multiplied by r_KL, where r_preference is the reward model score and r_KL is the KL divergence penalty weighted by a coefficient lambda.

RLHF vs. No RLHF: What the InstructGPT Numbers Actually Show

The InstructGPT paper remains the clearest empirical benchmark for understanding what RLHF actually delivers. Here are the findings that changed how I evaluate AI deployments.

Figure: InstructGPT vs. GPT-3 Performance Comparison

In 85% of head-to-head comparisons, the model with 1.3B parameters and trained with RLHF, was preferred over 175B GPT-3. That is approximately a 100x reduction in model size with a significant improvement in output quality from the perspective of the human evaluators.

When it comes to RLHF trained models, I most frequently cite the hallucination rate when talking to enterprise clients. InstructGPT hallucinated on closed-domain tasks 21% of the time, and GPT-3 did so 41% of the time. InstructGPT’s improvement, in the context of a product dealing with customer service or product compliance, which can have a hallucination rate that makes the product a liability, is significant.

InstructGPT also learned to follow the general safety instruction and, when asked to criticize itself, would, on average, produce 25% fewer outputs that could be classified as “toxic” compared with GPT-3.

In total, training the 175B PPO-ptx model took 60 petaflops/s-days, compared to 3,640 petaflops/s-days for the first GPT-3 instance. RLHF-Fine tuning is still costly, however, is by orders of magnitude a better investment than pretraining a larger model.

The Mistake People Make With RLHF 

Most teams think that instruction tuning and RLHF are the same and simply deploy models at the end of Stage 1.

Models that only have SFT are a big improvement from base models. They're better at following instructions. They appear aligned during demonstrations. However, they lack a reward model. They lack preference learning. Without those, a model can’t be conditioned to know the difference between technically responsive and user-preferred outputs.

Many of the AI systems I analyzed that internally advertise as “RLHF-aligned” are simply SFT-ing on the company’s proprietary data and calling it a day. The systems generate outputs that are both coherent and contextually relevant but, for the most part, miss expectations on the nuances of response, especially regarding tone and length as well as the use of appropriate level of hedging.

If your deployment of AI system skips reward modeling and lacks PPO, it is not RLHF. Rather, it is instruction-tuned. That distinction is particularly relevant in determining the reasons for the gap between user satisfaction and the system’s performance on benchmarks.

The Contrarian Take: More Human Feedback Does Not Mean Better Alignment

Common sense dictates that greater amounts of human preference data is beneficial. If more people annotated the data, created a better reward model with better alignment, it is reasonable to assume that it would be beneficial. However, in my experience, that is wrong, and in critical ways.

For InstructGPT’s reward model, the preferences of only 40 workers from Upwork and Scale AI were used. That is a very small amount of human preference data, especially for a model that is used by millions, and a very small sample of human values.

Scaling from 40 to 400 annotators is not a solution if all 400 annotators are from the same demographic. In this case, it's even more problematic. A reward model has learned a specific, unrepresentative slice of the population, but because of this, it has great confidence.

The issue is not the amount of annotators; it is the diversity of representation. A bias such as this is harder to identify than random noise because a diverse pool of annotators doesn't create an obvious error. They will make choices of framing, tone, and multiple assumptions which are explicit to some users and implicit to the rest.

I would perform an audit on the diversity of profession and demographics of the annotators, and only after, would I consider increasing the number of annotators. This is rarely part of the documentation provided by vendors.

What RLHF Looks Like in Real Enterprise AI Deployments in 2026

Most enterprises cannot afford to train RLHF pipelines from scratch. The required compute cost and infrastructure to buy annotators to build AI systems at the scale of OpenAI or Anthropic is beyond their reach. Enterprises do one of three things.

Option 1: Fine-tune on top of an already RLHF-aligned model. A model like GPT-4, Claude, or Llama 2 has RLHF in it already and the further alignment is done by SFT on data from a specific domain.

Option 2: RLAIF as analogue for RLHF. Reinforcement Learning from AI Feedback (RLAIF) replaces human evaluators with an AI model acting as evaluator. Anthropic's Constitutional AI idea is an example of this method. It is more scalable but adds an extra model-specific bias layer.

Option 3: Use lightweight preference collection in production. Instead of implementing a full-fledged RLHF pipeline, teams deploy their model and instrument it to gather indirect preference data (e.g. user edits, negative votes, session abandonment). The model is iteratively improved based on this data. This is slower and less optimal, but more cost-effective and better represents the user base.

All of these options come with tradeoffs. The correct option relies on your compute and annotation resources, and how much your domain diverges from the model's training distribution.

The Real Limitations of RLHF: What is Left Unsaid

There are cases where RLHF is bound to work. InstructGPT is an empirical example. However, some cases will show these techniques are not mature.

Reward hacking is one of the major failure modes. The model learns to game the system by getting a high score in the reward model rather than capturing the intent of the human evaluator. These two processes are generally aligned but diverge the more the model is optimized. Gao et al. (2022) analyzed this problem in their work, "Scaling Laws for Reward Model Overoptimization," and documented how reward model scores, at extreme levels of model optimization, become increasingly decoupled from human preferences.

Annotator disagreement also adds unavoidable noise to the reward model. It is well recognized that people disagree over which output is “better”, especially for ill-defined and open-ended tasks. Reward models have to deal with this disagreement and may assign high output scores, which may be interpreted as reward outputs, broadly, and may be scores that many users would in reality find significantly disagreeable.

Distribution shift between the annotation environment and production breaks alignment in ways that are hard to anticipate. Annotators work with specific prompt formats, interface designs, and task types. Your production users do not.

Scaling costs are another barrier. The costs of annotation, of training reward models, and the costs of multiple iterations of Proximal Policy Optimization (PPO) are hefty. For teams outside of Frontier Lab’s infrastructure, these costs define what is possible.

What I Would Actually Do

If I were to consult with an enterprise team RLHF implementation in 2026, I would place emphasis not on the model itself (which would be the typical starting point), but the process of selecting annotators.

I would demand that your team define what “preferred” (versus “dispreferred”) really means in your context before even a single fine-tuning iteration, as opposed to the general notion of alignment. I would insist this be formalized as a concrete framing (versus just a general alignment goal): who, what specific task, and in which conditions would your team define outputs as preferred (versus dispreferred). I would accept only those annotators who reflect the actual diversity of your anticipated users, as opposed to those who are available via a crowdsourcing service.

The answer is simple. The reward model can only incorporate what the annotators show. Every design element, starting from the KL penalty coefficient to the number of the PPO update step, is all shaped by the reward signal. If the reward model is constructed based on a small, unrepresentative sample of preferences, no matter the amount of sophisticated compute or algorithms, the alignment challenges will remain.

First, get the annotation base correct. Everything that follows is recoverable. A biased reward model will propagate its bias in every subsequent training step and will become harder to adjust. 

 

Frequently Asked Questions

What is RLHF used for, for AI systems?

In AI systems, RLHF is used to bring large language models in line with human preferences. Applications include increasing chatbot usefulness, naturalness, and reducing toxic/harmful speech and improving instruction-following and hallucination behavior. The RLHF component is foundational in the training of ChatGPT, Claude, and Llama 2, among others.

How is RLHF different from supervised fine-tuning?

Supervised fine-tuning (SFT) is the process of teaching a model to perform a task based on correct behavior examples. In RLHF, a reward model is also trained, but this time, the reward model is shaped by human preference comparisons and the learned policy is optimized via reinforcement learning. While SFT shows the model a task, RLHF shows the model a task that is preferred by a human. The two techniques are complimentary, with SFT being the earlier step in the RLHF process.

What are the three main limitations of RLHF?

The three main limitations are reward hacking where the model learns to hack the reward model instead of fulfilling the real human intent, annotator bias where the preference dataset models a small fragment of human values, and high costs of scaling since collecting higher quality preference data requires a higher cost than normal supervised training, as well as performing PPO optimization.

Does ChatGPT implement RLHF?

Yes, RLHF was applied by OpenAI in the training of InstructGPT, the predecessor of ChatGPT. The entire process was supervised training with human written examples, followed by reward model training with comparisons and PPO optimization. OpenAI cited this process in the InstructGPT paper (arXiv:2203.02155, 2022). ChatGPT built upon this procedure with more human feedback collected through the interface. 

 

Expert Opinion

At this stage RLHF is baseline necessary infrastructure for any deployment of production language models where the quality of the output and the trust of the users is relevant. The most important generalization of the InstructGPT findings is that for RLHF aligned models, smaller is bigger. The core metrics for users are more easily achieved with a smaller, more aligned model than with large, unaligned models. This has strong budgetary implications for companies. More budget has to be allocated to training/model alignment and less to the number of parameters.

The difficult parts of RLHF are not the algorithms. PPO is easy to implement. We know how to create reward model architectures. Defining what constitutes human preferences for your application, the costs of finding and hiring human annotators, and the costs of identifying reward exploitation before it starts to impact your system are the difficult parts of RLHF.

Here’s my condensed advice for people using RLHF in 2026: 

• Do not conflate SFT with RLHF. Complete the full three-stage pipeline before claiming alignment.
• Audit annotator diversity before scaling annotation volume.
• Monitor reward model scores in production over time. A score that rises while user satisfaction stays flat is a reward hacking signal.
• Budget for iterative reward model updates. The first version will not be the last version.

Remember, RLHF is a constant process. It is not an instant configuration. Teams that treat it as a once-off step, are the same teams who call me after six months, to ask why their models, which they think are aligned, have started to behave in a less aligned way. 

 

James Mitchell

He is the Founder and Editor-in-Chief of Techisane. He holds a Master of Science (MS) in Computer Science and a CISSP certification, with eight years of experience in enterprise technology. He began his career working with IT infrastructure before advancing into IT security and consulting. Mitchell brings firsthand experience to his writing, drawing on technologies he has implemented, tested, and worked with in real-world environments.