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What Is RLHF? Reinforcement Learning from Human Feedback

6 min. read
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RLHF (reinforcement learning from human feedback) trains models to follow human preferences by using human judgments as signals for reward. This makes outputs more consistent with expectations, but it also creates new risks if feedback is poisoned or manipulated.

 

How does RLHF work?

The title reads 'How RLHF works.' A horizontal sequence of six connected circles shows the steps: 'Begin with a pretrained model' with a gray icon of layered squares above 'Generate outputs for prompts'; 'Gather human preference data' with a blue chat bubble above 'Train a reward model'; 'Fine-tune with reinforcement learning' with an orange document above 'Add safeguards against drift'; 'Add safeguards against drift' with a teal looped arrow; 'Add safeguards against drift' with a purple shield and check mark; and 'Evaluate and iterate' with a dark gray gear.

Reinforcement learning from human feedback (RLHF) takes a pretrained model and aligns it more closely with human expectations.

It does this through a multi-step process:

  • People judge outputs
  • Those judgments train a reward model
  • Reinforcement learning then fine-tunes the base model using that signal

Here's how it works in practice.

Step 1: Begin with a pretrained model

The process starts with a large language model (LLM) trained on vast datasets using next-token prediction. This model serves as the base policy.

It generates fluent outputs but is not yet aligned with what humans consider helpful or safe.

Step 2: Generate outputs for prompts

The base model is given a set of prompts. It produces multiple candidate responses for each one.

The diagram shows a process beginning with a stack of documents labeled evaluation prompts with the note 'Many prompts sampled.' Arrows point downward to a box labeled base model (pretrained LLM) containing a neural network graphic. From this, an arrow leads to a text block labeled generated text with placeholder lorem ipsum text. An arrow then connects to a box labeled human preference ranking that includes an icon of three user figures. To the right, arrows branch out to five stacked colored circles arranged vertically in green, light green, yellow, orange, and red, labeled preference rankings. An arrow from these circles points upward to a blue box labeled reward model, which contains a bar chart graphic. A final arrow loops back from the reward model box to the text block, with the label train on {sample, reward} pairs. At the bottom of the diagram, a caption reads: 'Human-scored outputs are converted into ranked preferences to train a reward model for reinforcement learning.'

These outputs create the material that evaluators will compare.

Step 3: Gather human preference data

Human evaluators rank the candidate responses. Ranking provides clearer signals than rating each response alone.

A three-panel flow diagram titled 'Human feedback to reward model pipeline' shows a left teal panel labeled 'Human feedback' with two small chat-bubble cards titled 'Conversation A' and 'Conversation B' and a checklist labeled 'Which example is better?' with options 'Conversation A' and 'Conversation B.' An arrow labeled 'Binary preference feedback' points from this panel to a center orange panel labeled 'Reward model,' which repeats the two conversation cards under 'Examples' and includes an icon for 'Reward estimates.' A second arrow labeled 'Rewards for reinforcement learning' leads from the reward model to a right blue panel labeled 'Policy' containing a tool icon and the text 'Train the policy using reinforcement learning to maximize rewards.' Along the bottom, a return arrow labeled 'Conversation examples for evaluation' loops from the policy panel back toward the human feedback panel, completing the pipeline.

The result is a dataset of comparisons that show which outputs are more aligned with human expectations.

Step 4: Train a reward model

The preference dataset is used to train a reward model. This model predicts a numerical reward score for a given response.

In other words: It acts as a stand-in for human judgment.

Step 5: Fine-tune the policy with reinforcement learning

The base model is fine-tuned using reinforcement learning–most often proximal policy optimization (PPO).

The goal is to maximize the scores from the reward model. This step shifts the model toward producing outputs people prefer.

Note:
Although PPO is most often used, alternatives like DPO (direct preference optimization) or variants of policy gradient methods are emerging.

Step 6: Add safeguards against drift

A flow diagram titled 'Reward signals and safeguards in RLHF' shows three colored boxes under 'Model input.' The first is blue and reads 'The response begins clearly but trails off.' The second is yellow and reads 'The response is incomplete.' The third is red and reads 'The response is repetitive and unhelpful.' These connect to a central icon labeled 'Exploration,' which links to a brain-like icon labeled 'Feedback learning' with the note 'The system gives a mixed response with positive and negative sentiment.' From here, arrows flow into a gray box labeled 'Model outputs,' which has two categories: 'Human preference' and 'Output quality.' To the right, colored boxes show 'Reward score' values: 0.92, 0.58, and 0.13 in blue, yellow, and red above, and 0.87, 0.25, and 0.51 in blue, yellow, and red below.

Step 7: Evaluate and iterate

The process doesn’t stop after one round.

Models are tested, outputs are re-ranked, and the reward model can be updated. Iteration allows continuous refinement and helps uncover weaknesses.

To sum it up: RLHF builds on pretrained models by layering human judgment into training. Each step moves the model closer to outputs that are safe, useful, and appropriate for intended use.

 

Why is RLHF central to today's AI discussions?

RLHF is at the center of AI discussions because it directly shapes how models behave in practice.

“Reinforcement learning from human feedback (RLHF) has emerged as the central method used to finetune state‑of‑the‑art large language models (LLMs).”
- Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

It's not just a technical method. It's the main process that ties powerful LLMs to human expectations and social values.

Here's why that matters.

RLHF can be thought of as the method that connects a model's raw capabilities with the behaviors people expect.

The thing about large language models is that they're powerful–but unpredictable. RLHF helps nudge LLMs toward responses that people judge as safe, useful, or trustworthy. That's why AI researchers point to RLHF as one of the few workable tools for making models practical at scale.

But the process depends on preference data. Preference data is the rankings people give when comparing different outputs. For example, evaluators might mark one response as clearer or safer than another. Which means the values and perspectives of evaluators shape the results.

A flow diagram titled 'How biased preference data skews model outputs' shows a blue circle on the left labeled 'RLHF outputs' feeding into a process box labeled 'Train (Biased preference data)' with a brain icon and a warning triangle. Below, a connected box labeled 'Validate (Biased preference data)' contains a head icon with a warning triangle. Arrows loop between the train and validate boxes, and an arrow extends right from the train box to a dark circle labeled 'Test' with a checklist icon. A dotted line also connects the train box directly to the test circle.

So bias is always present. Sometimes that bias reflects narrow cultural assumptions. Other times it misses the diversity of views needed for global use. And that has raised concerns about whether RLHF can fairly represent different communities.

The impact extends beyond research labs.

How RLHF is applied influences whether AI assistants refuse dangerous instructions, how they answer sensitive questions, and how reliable they seem to end users.

In short: It determines how much trust people place in AI systems and whose values those systems reflect.

 

What role does RLHF play in large language models?

Large language models are first trained on massive datasets. This pretraining gives them broad knowledge of language patterns. But it doesn't guarantee that their outputs are useful, safe, or aligned with human goals.

RLHF is the technique that bridges this gap.

It takes a pretrained model and tunes it to follow instructions in ways that feel natural and helpful. Without this step, the raw model might generate text that is technically correct but irrelevant, incoherent, or even unsafe.

A labeled diagram titled 'RLHF in the LLM training pipeline' is divided into three sections: low-quality data, high-quality data, and human feedback. On the left, a red cylinder labeled 'Pretraining data' shows text 'Optimized for text completion' and flows into an orange box labeled 'Pretraining,' which connects to a pale red rectangle labeled 'Base model.' In the middle, a green cylinder labeled 'Instruction data (optional)' shows text 'Fine-tuned for dialogue' and flows into a green box labeled 'Supervised fine-tuning (SFT) (optional),' which connects to a pale green rectangle labeled 'SFT model.' On the right, under a shaded box labeled 'RLHF,' a turquoise cylinder labeled 'Preference comparisons' flows into a turquoise box labeled 'Reward model training (preference learning),' which connects to a pale blue rectangle labeled 'Reward model.' Next to it, another turquoise cylinder labeled 'Prompts' shows text 'Optimized to generate responses that maximize scores by reward model' and flows into a turquoise box labeled 'Reinforcement learning (e.g., PPO).' This box connects to a pale blue rectangle labeled 'Aligned chat model.' Arrows connect the SFT model and base model to the RLHF section, showing integration of steps.

In other words:

RLHF transforms a general-purpose system into one that can respond to prompts in a way people actually want.

Why is this needed?

Scale. Modern LLMs have billions of parameters and can generate endless possibilities. Rule-based filtering alone cannot capture the nuances of human preference. RLHF introduces a human-guided reward signal, making the model's outputs more consistent with real-world expectations.

Another reason is safety. Pretrained models can reproduce harmful or biased content present in their training data. Human feedback helps steer outputs away from these risks, though the process is not perfect. Biases in judgments can still carry through.

Finally, RLHF makes LLMs more usable. It reduces the burden on users to constantly reframe or correct prompts. Instead, the model learns to provide responses that are direct, structured, and more contextually appropriate.

Basically, RLHF is what enables large language models to function as interactive systems rather than static text generators. It's the method that makes them practical, adaptable, and in step with human expectations.

Though not without ongoing challenges.

 

What are the limitations of RLHF?

A diagram titled 'Limitations of RLHF' shows three circular icons with text below each. On the left, a blue circle with four arrows pointing outward represents 'Scalability,' with text reading 'Gathering quality human feedback at the scale of modern LLMs is slow, costly, and difficult to sustain.' In the center, a blue circle with two overlapping chat bubbles represents 'Inconsistent feedback,' with text reading 'Annotators often disagree or vary over time, introducing noise that weakens training stability.' On the right, a blue circle with a balanced scale represents 'Helpful vs. harmless trade-offs,' with text reading 'Optimizing for helpfulness risks oversharing, while prioritizing harmlessness can block safe, useful outputs.'

Reinforcement learning from human feedback is powerful, but not perfect.

Researchers have pointed out clear limitations in how it scales, how reliable the feedback really is, and how trade-offs are managed between usefulness and safety.

"RLHF faces fundamental limitations in its ability to fully capture human values and align behavior, raising questions about its scalability as models grow."
- Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

These challenges shape ongoing debates about whether RLHF can remain the default approach for aligning large models.

Let's look more closely at three of the biggest concerns.

Scalability

RLHF depends heavily on human feedback. That makes it resource-intensive.

For large models, gathering enough quality feedback is slow and costly. Scaling this process across billions of parameters introduces practical limits.

Note:
Researchers note that current methods may not be sustainable for future models of increasing size. For this reason, new approaches are being explored to make alignment more scalable for increasingly powerful systems.

Inconsistent feedback

Human feedback is not always consistent.

Annotators may disagree on what constitutes an appropriate response. Even the same person can vary in judgment over time.

This inconsistency creates noise in the training process. Which means: Models may internalize preferences that are unstable or poorly defined.

“Helpful vs. harmless” trade-offs

RLHF often balances two competing goals. Models should be helpful to the user but also avoid harmful or unsafe outputs.

These goals sometimes conflict.

A system optimized for helpfulness may overshare sensitive information. One optimized for harmlessness may refuse useful but safe answers.

The trade-off remains unresolved.

Note:
Trade-offs are highly context dependent. What counts as safe or useful can change across domains, and adversarial inputs can push models toward one extreme. This variability makes it difficult to design a single balance point that holds across all use cases.

 

What is the difference between RLHF and reinforcement learning?

Reinforcement learning (RL) is a machine learning method where an agent learns by interacting with an environment. It tries actions, receives rewards or penalties, and updates its strategy. Over time, it improves by maximizing long-term reward.

A diagram titled 'Reinforcement learning (RL)' shows a loop between an agent and an environment. On the left, the agent is depicted inside a box containing two components: a dark blue square labeled 'Policy' with an arrow pointing right to 'Action,' and a teal square labeled 'Reinforcement learning algorithm' with a brain icon. A downward arrow labeled 'Policy update' connects the policy to the reinforcement learning algorithm. On the right, a large gray rectangle labeled 'Environment' contains icons of a computer screen, database, and people. A right-facing arrow labeled 'Action' connects the policy to the environment. A left-facing arrow labeled 'Reward' connects the environment back to the reinforcement learning algorithm. A second left-facing arrow labeled 'Observation' connects the environment back to the policy.

RLHF builds on this foundation. Instead of using a fixed reward signal from the environment, it uses human feedback to shape the reward model. Essentially: People provide judgments on model outputs. These judgments train a reward model, which then guides the reinforcement learning process.

A diagram titled 'Reinforcement learning from human feedback (RLHF)' is divided into three sections: pretraining, human feedback, and fine-tuning. On the left, the pretraining section shows a purple cylinder labeled 'Pretraining data' feeding into an icon of a lightbulb labeled 'Self-supervised learning,' which connects to a light blue box labeled 'Base LLM (pretrained).' An arrow from the base LLM flows downward into a dark blue circle labeled 'Supervised fine-tuning,' which then points right into a gray box labeled 'Aligned LLM (fine-tuned).' In the top right, the human feedback section shows a teal cylinder labeled 'Human preference data' with arrows pointing right toward two icons: a shield labeled 'Safety reward model' and a thumbs-up labeled 'Helpful reward model.' The human preference data also points downward into the aligned LLM. On the far right, the RLHF section shows a gray box with two stacked items: 'Rejection sampling' and 'Proximity policy optimization,' connected by arrows to the aligned LLM.

This change matters.

Standard RL depends on clear, predefined rules in the environment. RLHF adapts RL for complex tasks—like language generation—where human preferences are too nuanced to reduce to fixed rules.

The result is a system better aligned with what people consider useful, safe, or contextually appropriate.

 

What are the security risks of RLHF?

A diagram titled 'RLHF security risks' has six labeled boxes arranged in two columns. On the left, the first red square contains an icon of interconnected nodes with the label 'Misaligned reward models.' Below it, a red square with a warning symbol inside a dotted circle is labeled 'Feedback data poisoning.' At the bottom, another red square with a document and warning triangle icon is labeled 'Exploitable trade-offs.' On the right, the top red square shows a slider control icon labeled 'Over-reliance on alignment.' Below it, a red square with a megaphone icon is labeled 'Bias amplification.'

RLHF improves model alignment, but it was never designed as a security control. Because it relies on human judgment and reward models, it introduces new attack surfaces that adversaries can exploit.

Key risks include:

  • Misaligned reward models. Human feedback can be inconsistent or biased, leading reward models to reinforce unsafe or unintended behaviors.
  • Feedback data poisoning. Malicious raters can manipulate preference signals, skewing model behavior even with a small fraction of poisoned data.
  • Exploitable trade-offs. Balancing helpfulness with harmlessness can be gamed, with attackers disguising harmful queries as benign or forcing refusals that reduce usability.
  • Over-reliance on alignment. Jailbreak prompts and adversarial inputs can bypass RLHF guardrails, showing that alignment alone is brittle without layered defenses.
  • Bias amplification. Human judgments reflect social and cultural biases, which RLHF can reproduce and amplify over time.

These weaknesses carry business implications. Poisoned or manipulated models can expose sensitive data, spread inaccurate information, or deliver unsafe outputs to customers. Beyond immediate misuse, biased or harmful outputs create reputational risk, and regulators may impose penalties if safeguards are lacking.

In short: RLHF is valuable for shaping model behavior, but without complementary defenses such as red teaming, input filtering, and validation, it can create operational, reputational, and regulatory exposure that organizations cannot ignore.

| Further reading:

Learn how prompt attacks manipulate outputs and reshape GenAI responses in Securing GenAI: A Comprehensive Report on Prompt Attacks, Risks, and Solutions.

Download report

 

RLHF FAQs

In ChatGPT, RLHF refines responses using human feedback. Human raters compare model outputs, and those preferences train a reward model. The model then learns through reinforcement learning to generate outputs that align more closely with user expectations.
RLHF is used to align AI behavior with human values and expectations. It helps models respond in ways that are helpful, safe, and contextually appropriate. The process reduces harmful outputs and improves reliability compared to models trained without human feedback.
Reinforcement learning (RL) uses numerical rewards from the environment. RLHF replaces that signal with human feedback, collected through preference comparisons. This lets models learn from subjective human judgments instead of only objective measures, making it more suitable for aligning large language models.
At OpenAI, RLHF is the method used to fine-tune models like ChatGPT. Human feedback guides a reward model, which then directs reinforcement learning. The approach helps ensure outputs are more aligned with safety, usability, and user intent.
Supervised learning uses fixed examples. RLHF instead adapts models using human preferences, which capture nuance and context that static labels miss. This makes RLHF more effective for aligning open-ended language tasks where “correct” answers are subjective.
RLHF helps models become safer and more useful. It reduces harmful or irrelevant outputs, captures nuance in language, and aligns systems with human expectations. These advantages make it widely used for fine-tuning large language models.
RLHF improves model alignment but is not a complete safety solution. Because it depends on subjective human feedback and reward models, it can introduce vulnerabilities such as bias, poisoning, or over-reliance. Organizations should treat RLHF as one layer in a broader AI security strategy.
Many large language models, including ChatGPT, Claude, and Gemini use RLHF to refine outputs. The technique is also applied in other advanced AI systems that require alignment with human preferences. It has become a common practice in industry, though implementation details differ across developers.
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