Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for aligning AI systems, especially generative AI models like large language models (LLMs) with human expectations and values. By incorporating human preferences into the training loop, RLHF helps AI produce outputs that are more helpful, safe, and contextually appropriate.
This article provides a deep dive into RLHF: what it is, its benefits and limitations, when and how it fits into an AI product’s development, the tools used to implement it, and strategies for recruiting human participants to provide the critical feedback that drives RLHF. In particular, we will highlight why effective human recruitment (and platforms like BetaTesting) is crucial for RLHF success.
Here’s what we will explore:
- What is RLHF?
- Benefits of RLHF
- Limitations of RLHF
- When Does RLHF Occur in the AI Development Timeline?
- Tools Used for RLHF
- How to Recruit Humans for RLHF
What is RLHF?
Reinforcement learning from human feedback (RLHF) is a machine learning technique in which a “reward model” is trained with direct human feedback, then used to optimize the performance of an artificial intelligence agent through reinforcement learning – IBM
In essence, humans guide the AI by indicating which outputs are preferable, and the AI learns to produce more of those preferred outputs. This method is especially useful for tasks where the notion of “correct” output is complex or subjective.
For example, it would be impractical (or even impossible) for an algorithmic solution to define ‘funny’ in mathematical terms – but easy for humans to rate jokes generated by a large language model (LLM). That human feedback, distilled into a reward function, could then be used to improve the LLM’s joke writing abilities. In such cases, RLHF allows us to capture human notions of quality (like humor, helpfulness, or style) which are hard to encode in explicit rules.
Originally demonstrated on control tasks (like training agents to play games), RLHF gained prominence in the realm of LLMs through OpenAI’s research. Notably, the InstructGPT model was fine-tuned with human feedback to better follow user instructions, outperforming its predecessor GPT-3 in both usefulness and safety.
This technique was also key to training ChatGPT – “when developing ChatGPT, OpenAI applies RLHF to the GPT model to produce the responses users want. Otherwise, ChatGPT may not be able to answer more complex questions and adapt to human preferences the way it does today.” In summary, RLHF is a method to align AI behavior with human preferences by having people directly teach the model what we consider good or bad outputs.
Check it out: We have a full article on AI Product Validation With Beta Testing
Benefits of RLHF
Incorporating human feedback into AI training brings several important benefits, especially for generative AI systems:
- Aligns output with human expectations and values: By training on human preferences, AI models become “cognizant of what’s acceptable and ethical human behavior” and can be corrected when they produce inappropriate or undesired outputs.
In practice, RLHF helps align models with human values and user intent. For instance, a chatbot fine-tuned with RLHF is more likely to understand what a user really wants and stick within acceptable norms, rather than giving a literal or out-of-touch answer. - Produces less harmful or dangerous output: RLHF is a key technique for steering AI away from toxic or unsafe responses. Human evaluators can penalize outputs that are offensive, unsafe, or factually wrong, which trains the model to avoid them.
As a result, RLHF-trained models like InstructGPT and ChatGPT generate far fewer hateful, violent, or otherwise harmful responses compared to uninstructed models. This fosters greater trust in AI assistants by reducing undesirable outputs. - More engaging and context-aware interactions: Models tuned with human feedback provide responses that feel more natural, relevant, and contextually appropriate. Human raters often reward outputs that are coherent, helpful, or interesting.
OpenAI reported that RLHF-tuned models followed instructions better, maintained factual accuracy, and avoided nonsense or “hallucinations” much more than earlier models. In practice, this means an RLHF-enhanced AI can hold more engaging conversations, remember context, and respond in ways that users find satisfying and useful. - Ability to perform complex tasks aligned with human understanding: RLHF can unlock a model’s capability to handle nuanced or difficult tasks by teaching it the “right” approach as judged by people. For example, humans can train an AI to summarize text in a way that captures the important points, or to write code that actually works, by giving feedback on attempts.
This human-guided optimization enables LLMs with lesser parameters to perform better on challenging queries. OpenAI noted that its labelers preferred outputs from the 1.3B-parameter version of InstructGPT over even outputs from the 175B-parameter version of GPT-3. – showing that targeted human feedback can beat brute-force scale in certain tasks.
Overall, RLHF allows AI to tackle complex, open-ended tasks in ways that align with what humans consider correct or high-quality.
Limitations of RLHF
Despite its successes, RLHF also comes with notable challenges and limitations:
- Expensive and resource-intensive: Obtaining high-quality human preference data is costly and does not easily scale. Human preference data is expensive. The need to gather firsthand human input can create a costly bottleneck that limits the scalability of the RLHF process.
Training even a single model can require thousands of human feedback judgments, and employing experts or large crowds of annotators can drive up costs. This is one reason companies are researching partial automation of the feedback process (for example, AI-generated feedback as a supplement) to reduce reliance on humans. - Subjective and inconsistent feedback: Human opinions on what constitutes a “good” output can vary widely.
“Human input is highly subjective. It’s difficult, if not impossible, to establish firm consensus on what constitutes ‘high-quality’ output, as human annotators will often disagree… on what ‘appropriate’ model behavior should mean.”
In other words, there may be no single ground truth for the model to learn, and feedback can be noisy or contradictory. This subjectivity makes it hard to perfectly optimize to “human preference,” since different people prefer different things. - Risk of bad actors or trolling: RLHF assumes feedback is provided in good faith, but that may not always hold. Poorly incentivized crowd workers might give random or low-effort answers, and malicious users might try to teach the model undesirable behaviors.
Researchers have even identified “troll” archetypes who give harmful or misleading feedback. Robust quality controls and careful participant recruitment are needed to mitigate this issue (more on this in the recruitment section below). - Bias and overfitting to annotators: An RLHF-tuned model will reflect the perspectives and biases of those who provided the feedback. If the pool of human raters is narrow or unrepresentative, the model can become skewed.
For example, a model tuned only on Western annotators’ preferences might perform poorly for users from other cultures. It’s essential to use diverse and well-balanced feedback sources to avoid baking in bias.
In summary, RLHF improves AI alignment but is not a silver bullet – it demands significant human effort, good experimental design, and continuous vigilance to ensure the feedback leads to better, not worse, outcomes.
When Does RLHF Occur in the AI Development Timeline?
RLHF is typically applied after a base AI model has been built, as a fine-tuning and optimization stage in the AI product development lifecycle. By the time you’re using RLHF, you usually have a pre-trained model that’s already learned from large-scale data; RLHF then adapts this model to better meet human expectations.
The RLHF pipeline for training a large language model usually involves multiple phases:
- Supervised fine-tuning of a pre-trained model: Before introducing reinforcement learning, it’s common to perform supervised fine-tuning (SFT) on the model using example prompts and ideal responses.
This step “primes” the model with the format and style of responses we want. For instance, human trainers might provide high-quality answers to a variety of prompts (Q&A, writing tasks, etc.), and the model is tuned to imitate these answers.
SFT essentially “‘unlocks’ capabilities that GPT-3 already had, but were difficult to elicit through prompt engineering alone. In other words, it teaches the model how it should respond to users before we start reinforcement learning. - Reward model training (human preference modeling): Next, we collect human feedback on the model’s outputs to train a reward model. This usually involves showing human evaluators different model responses and having them rank or score which responses are better.
For example, given a prompt, the model might generate multiple answers; humans might prefer Answer B over Answer A, etc. These comparisons are used to train a separate neural network – the reward model – that takes an output and predicts a reward score (how favorable the output is).
Designing this reward model is tricky because asking humans to give absolute scores is hard; using pairwise comparisons and then mathematically normalizing them into a single scalar reward has proven effective. The reward model effectively captures the learned human preferences. - Policy optimization via reinforcement learning: In the final phase, the original model (often called the “policy” in RL terms) is further fine-tuned using reinforcement learning algorithms, with the reward model providing the feedback signal.
A popular choice is Proximal Policy Optimization (PPO), which OpenAI used for InstructGPT and ChatGPT. The model generates outputs, the reward model scores them, and the model’s weights are adjusted to maximize the reward. Care is taken to keep the model from deviating too much from its pre-trained knowledge (PPO includes techniques to prevent the model from “gaming” the reward by producing gibberish that the reward model happens to score highly.
Through many training iterations, this policy optimization step trains the model to produce answers that humans (as approximated by the reward model) would rate highly. After this step, we have a final model that hopefully aligns much better with human-desired outputs.
It’s worth noting that pre-training (the initial training on a broad dataset) is by far the most resource-intensive part of developing an LLM. The RLHF fine-tuning stages above are relatively lightweight in comparison – for example, OpenAI reported that the RLHF process for InstructGPT used <2% of the compute that was used to pre-train GPT-3.
RLHF is a way to get significant alignment improvements without needing to train a model from scratch or use orders of magnitude more data; it leverages a strong pre-trained foundation and refines it with targeted human knowledge.
Check it out: Top 10 AI Terms Startups Need to Know
Tools Used for RLHF
Implementing RLHF for AI models requires a combination of software frameworks, data collection tools, and evaluation methods, as well as platforms to source the human feedback providers. Key categories of tools include:
Participant recruitment platforms: A crucial “tool” for RLHF is the source of human feedback providers. You need humans (often lots of them) to supply the preferences, rankings, and demonstrations that drive the whole process. This is where recruitment platforms come in (discussed in detail in the next section).
In brief, some options include crowdsourcing marketplaces like Amazon Mechanical Turk, specialized AI data communities, or beta testing platforms to get real end-users involved. The quality of the human feedback is paramount, so choosing the right recruitment approach (and platform) significantly impacts RLHF outcomes.
BetaTesting is a platform with a large community of vetted, real-world testers that can be tapped for collecting AI training data and feedback at scale
Other services like Pareto or Surge AI maintain expert labeler networks to provide high-accuracy RLHF annotations, while platforms like Prolific recruit diverse participants who are known for providing attentive and honest responses. Each has its pros and cons, which we’ll explore below.
RLHF training frameworks and libraries: Specialized libraries help researchers train models with RLHF algorithms. For example, Hugging Face’s TRL (Transformer Reinforcement Learning) library provides “a set of tools to train transformer language models” with methods like supervised fine-tuning, reward modeling, and PPO/other optimization algorithms.
Open-source frameworks such as DeepSpeed-Chat (by Microsoft), ColossalChat (by Colossal AI), and newer projects like OpenRLHF have emerged to facilitate RLHF at scale. These frameworks handle the complex “four-model” setup (policy, reward model, reference model, optimizer) and help with scaling to large model sizes. In practice, teams leveraging RLHF often start with an existing library rather than coding the RL loop from scratch.
Data labeling & annotation tools: Since RLHF involves collecting a lot of human feedback data (e.g. comparisons, ratings, corrections), robust annotation tools are essential. General-purpose data labeling platforms like Label Studio and Encord now offer templates or workflows specifically for collecting human preference data for RLHF. These tools provide interfaces for showing prompts and model outputs to human annotators and recording their judgments.
Many organizations also partner with data service providers: for instance, Appen (a data annotation company) has an RLHF service that leverages a carefully curated crowd of diverse human annotators with domain expertise to supply high-quality feedback. Likewise, Scale AI offers an RLHF platform with an intuitive interface and collaboration features to streamline the feedback process for labelers.
Such platforms often come with built-in quality control (consistency checks, gold standard evaluations) to ensure the human data is reliable.
Evaluation tools and benchmarks: After fine-tuning a model with RLHF, it’s critical to evaluate how much alignment and performance have improved. This is done through a mix of automated benchmarks and further human evaluation.
A notable tool is OpenAI Evals, an open-source framework for automated evaluation of LLMs. Developers can define custom evaluation scripts or use community-contributed evals (covering things like factual accuracy, reasoning puzzles, harmlessness tests, etc.) to systematically compare their RLHF-trained model against baseline models. Besides automated tests, one might run side-by-side user studies: present users with responses from the new model vs. the old model or a competitor, and ask which they prefer.
OpenAI’s launch of GPT-4, for example, reported that RLHF doubled the model’s accuracy on challenging “adversarial” questions – a result discovered through extensive evaluation. Teams also monitor whether the model avoids the undesirable outputs it was trained against (for instance, testing with provocative prompts to see if the model stays polite and safe).
In summary, evaluation tools for RLHF range from code-based benchmarking suites to conducting controlled beta tests with real people in order to validate that the human feedback truly made the model better.
How to Recruit Humans for RLHF
Obtaining the “human” in the loop for RLHF can be challenging – the task requires people who are thoughtful, diligent, and ideally somewhat knowledgeable about the context.
As one industry source notes,
“unlike typical data-labeling tasks, RLHF demands in-depth and honest feedback. The people giving that feedback need to be engaged, invested, and ready to put the time and effort into their answers.”
This means recruiting the right participants is crucial. Here are some common strategies for recruiting humans for RLHF projects, and how they stack up:
Internal recruitment (employees or existing users): One way to get reliable feedback is to recruit from within your organization or current user base. For example, a product team might have employees spend time testing a chatbot and providing feedback, or invite power-users of the product to give input.
The advantage is that these people often have domain expertise and a strong incentive to improve the AI. They might also understand the company’s values well (helpful for alignment). However, internal pools are limited in size and can introduce bias – employees might think alike, and loyal customers might not represent the broader population.
This approach works best in early stages or for niche tasks where only a subject-matter expert can evaluate the model. It’s essentially a “friends-and-family” beta test for your AI.
Social media, forums, and online communities: If you have an enthusiastic community or can tap into AI discussion forums, you may recruit volunteers. Announcing an “AI improvement program” on Reddit, Discord, or Twitter, for instance, can attract people interested in shaping AI behavior.
A notable example is the OpenAssistant project, which crowd-sourced AI assistant conversations from over 13,500 volunteers worldwide. These volunteers helped create a public dataset for RLHF, driven by interest in an open-source ChatGPT alternative. Community-driven recruitment can yield passionate contributors, but keep in mind the resulting group may skew towards tech-savvy or specific demographics (not fully representative).
Also, volunteers need motivation – many will do it for altruism or curiosity, but retention can be an issue without some reward or recognition. This approach can be excellent for open projects or research initiatives where budget is limited but community interest is high.
Paid advertising and outreach: Another route is to recruit strangers via targeted ads or outreach campaigns. For instance, if you need doctors to provide feedback for a medical AI, you might run LinkedIn or Facebook ads inviting healthcare professionals to participate in a paid study. Or more generally, ads can be used to direct people to sign-up pages to become AI model “testers.”
This method gives you control over participant criteria (through ad targeting) and can reach people outside existing platforms. However, it requires marketing effort and budget, and conversion rates can be low (not everyone who clicks an ad will follow through to do tedious feedback tasks). It’s often easier to leverage existing panels and platforms unless you need a very specific type of user that’s hard to find otherwise.
If using this approach, clarity in the ad (what the task is, why it matters, and that it’s paid or incentivized) will improve the quality of recruits by setting proper expectations.
Participant recruitment platforms: In many cases, the most efficient solution is to use a platform specifically designed to find and manage participants for research or testing. Several such platforms are popular for RLHF and AI data collection:
- BetaTesting: is a user research and beta-testing platform with a large pool of over 450,000 vetted participants across various demographics, devices, and locations.
We specialize in helping companies collect feedback, bug reports, and “human-powered data for AI” from real-world users. The platform allows targeting by 100+ criteria (age, gender, tech expertise, etc.) and supports multi-day or iterative test campaigns.
For RLHF projects, BetaTesting can recruit a cohort of testers who interact with your AI (e.g., try prompts and rate responses) in a structured way. Because the participants are pre-vetted and the process is managed, you often get higher-quality feedback than a general crowd marketplace. BetaTesting’s focus on real user experience means participants tend to give more contextual and qualitative feedback, which can enrich RLHF training (for instance, explaining why a response was bad, not just rating it).
In practice, BetaTesting is an excellent choice when you want high-quality, diverse feedback at scale without having to build your own community from scratch – the platform provides the people and the infrastructure to gather their input efficiently.
- Pareto (AI): is a service that offers expert data annotators on demand for AI projects, positioning itself as a premium solution for RLHF and other data needs. Their approach is more hands-on – they assemble a team of trained evaluators for your project and manage the process closely.
Pareto emphasizes speed and quality, boasting “expert-vetted data labelers” and “industry-leading accuracy” in fine-tuning LLMs. Clients define the project and Pareto’s team executes it, including developing guidelines and conducting rigorous quality assurance. This is akin to outsourcing the human feedback loop to professionals.
It can be a great option if you have the budget and need very high-quality, domain-specific feedback (for example, fine-tuning a model in finance or law with specialists, ensuring consistent and knowledgeable ratings). The trade-off is cost and possibly less transparency or control compared to running a crowdsourced approach. For many startups or labs, Pareto might be used on critical alignment tasks where errors are costly.
- Prolific: is an online research participant platform initially popular in academic research, now also used for AI data collection. Prolific maintains a pool of 200,000+ active participants who are pre-screened and vetted for quality and ethics. Researchers can easily set up studies and surveys, and Prolific handles recruiting participants that meet the study’s criteria.
For RLHF, Prolific has highlighted its capability to provide “a diverse pool of participants who give high-quality feedback on AI models” – the platform even advertises use cases like tuning AI with human feedback. The key strengths of Prolific are data quality and participant diversity. Studies (and Prolific’s own messaging) note that Prolific respondents tend to pay more attention and give more honest, detailed answers than some other crowdsourcing pools.
The platform also makes it easy to integrate with external tasks: you can, for example, host an interface where users chat with your model and rate it, and simply give Prolific participants the link. If your RLHF task requires thoughtful responses (e.g., writing a few sentences explaining preferences) and you want reliable people, Prolific is a strong choice.
The costs are higher per participant than Mechanical Turk, but you often get what you pay for in terms of quality. Prolific also ensures participants are treated and paid fairly, which is ethically important for long-term projects.
- Amazon Mechanical Turk (MTurk): is one of the oldest and largest crowd-work platforms, offering access to a vast workforce to perform micro-tasks for modest pay. Many early AI projects (and some current ones) have used MTurk to gather training data and feedback.
On the plus side, MTurk can deliver fast results at scale – if you post a simple RLHF task (like “choose which of two responses is better” with clear instructions), you could get thousands of judgments within hours, given the size of the user base. It’s also relatively inexpensive per annotation. However, the quality control burden is higher: MTurk workers vary from excellent to careless, and without careful screening and validation you may get noisy data. For nuanced RLHF tasks that require reading long texts or understanding context, some MTurk workers may rush through just to earn quick money, which is problematic.
Best practices include inserting test questions (to catch random answers), requiring a qualification test, and paying sufficiently to encourage careful work. Scalability can also hit limits if your task is very complex – fewer Turkers might opt in.
It’s a powerful option for certain types of feedback (especially straightforward comparisons or binary acceptability votes) and has been used in notable RLHF implementations. But when ultimate quality and depth of feedback are paramount, many teams now prefer curated platforms like those above. MTurk remains a useful tool in the arsenal, particularly if used with proper safeguards and for well-defined labeling tasks.
Each recruitment method can be effective, and in fact many organizations use a combination. For example, you might start with internal experts to craft an initial reward model, then use a platform like BetaTesting to get a broader set of evaluators for scaling up, and finally run a public-facing beta with actual end-users to validate the aligned model in the wild. The key is to ensure that your human feedback providers are reliable, diverse, and engaged, because the quality of the AI’s alignment is only as good as the data it learns from.
No matter which recruitment strategy you choose, invest in training your participants and maintaining quality. Provide clear guidelines and examples of good vs. bad outputs. Consider starting with a pilot: have a small group do the RLHF task, review their feedback, and refine instructions before scaling up. Continuously monitor the feedback coming in – if some participants are giving random ratings, you may need to replace them or adjust incentives.
Remember that RLHF is an iterative, ongoing process (“reinforcement” learning is never really one-and-done). Having a reliable pool of humans to draw from – for initial training and for later model updates – can become a competitive advantage in developing aligned AI products.
Check it out: We have a full article on AI in User Research & Testing in 2025: The State of The Industry
Conclusion
RLHF is a powerful approach for making AI systems more aligned with human needs, but it depends critically on human collaboration. By understanding where RLHF fits into model development and leveraging the right tools and recruitment strategies, product teams and researchers can ensure their AI not only works, but works in a way people actually want.
With platforms like BetaTesting and others making it easier to harness human insights, even smaller teams can implement RLHF to train AI models that are safer, more useful, and more engaging for their users.
As AI continues to evolve, keeping humans in the loop through techniques like RLHF will be vital for building technology that genuinely serves and delights its human audience.
Have questions? Book a call in our call calendar.
BetaTesting Blog 