RLHF (reinforcement learning from human feedback) is a training method that aligns a large language model with human preferences by having people rank or rate model outputs, training a reward model on those judgments, and then optimising the language model against that reward signal. In short, RLHF teaches a model not just to predict the next word, but to produce answers that humans actually prefer: more helpful, more honest and less harmful. It is the technique behind the jump in quality from raw GPT-style base models to the assistants people use every day.
If you are building or fine-tuning an LLM and need humans to rank responses, write demonstrations, or red-team outputs, you need an RLHF team. Below we define the key terms, walk through how RLHF works, and explain who provides RLHF teams, including how OSCABE delivers managed RLHF pods trained on your own rubric.
What is RLHF, in plain English?
RLHF turns fuzzy human preferences into a numerical signal a model can be optimised against. A base language model predicts text well, but it has no inherent sense of what makes one answer better than another. RLHF supplies that sense by collecting human judgments at scale and folding them into training.
The technique was popularised by OpenAI's InstructGPT work, summarised in OpenAI's research on aligning language models to follow instructions. The core insight is simple: if humans consistently say "response A is better than response B", you can train a model to generate more A-like answers.
Definition (RLHF): Reinforcement learning from human feedback is a machine-learning approach where human preference judgments are used to train a reward model, which then guides reinforcement-learning optimisation of a target model so its outputs better match what humans want.
How does RLHF work, step by step?
RLHF is usually described as a three-stage pipeline. Each stage needs different human input, and the quality of that human input largely determines the quality of the final model.
Stage 1: Supervised fine-tuning (SFT)
Definition (SFT): Supervised fine-tuning trains a base model on curated example pairs of prompts and high-quality responses written or approved by humans, teaching the model the desired format, tone and behaviour before any reinforcement learning happens.
Here, human writers produce demonstration data: ideal answers to representative prompts, which the model learns to imitate. Domain expertise matters most at this stage, because a demonstration is only as good as the person writing it. A medical SFT example written by a non-clinician can teach the model the wrong thing.
Stage 2: Train a reward model on preference data
Definition (preference data): Preference data consists of human comparisons between two or more model outputs for the same prompt, where annotators indicate which response is better (and sometimes by how much). These rankings are the training signal for the reward model.
Annotators rank several candidate responses, and those rankings train a separate reward model that learns to predict a scalar "human preference" score for any response. The reward model is, in effect, a learned proxy for human judgment.
Stage 3: Optimise with reinforcement learning (PPO or DPO)
The language model is then optimised so its outputs score highly under the reward model. Two common approaches:
- PPO (Proximal Policy Optimisation): a reinforcement-learning algorithm that updates the model in small, stable steps so it earns higher reward without drifting too far from its original behaviour. See the PPO paper on arXiv for the original method.
- DPO (Direct Preference Optimisation): a newer, simpler method that optimises directly on preference pairs without training a separate reward model or running full RL, described in the DPO paper on arXiv.
In all cases, humans remain the source of truth. The algorithms differ; the bottleneck is consistent, high-quality human feedback.
Why do you need a dedicated RLHF team?
You cannot crowdsource alignment cheaply and expect a strong model. Both the literature and practitioner consensus point the same way: model quality is gated by annotation quality. You need people who understand your rubric, stay consistent across thousands of judgments, and have genuine domain expertise where the task demands it.
A good RLHF team handles several distinct jobs:
| RLHF task | What the team does | Who is best suited |
|---|---|---|
| Demonstration / SFT writing | Author ideal responses to prompts | Domain experts and strong writers |
| Preference ranking | Compare and rank candidate outputs | Trained, calibrated annotators |
| Reward-model labelling | Produce consistent scalar/comparative labels | Calibrated annotators with QA |
| Red-teaming | Probe the model for unsafe or wrong outputs | Adversarial specialists |
| Coding RLHF | Review and rank code completions | CE-verified / IIT-trained engineers |
| Domain evaluation | Judge correctness in law, medicine, finance | Chartered / certified professionals |
Who provides RLHF teams?
There are three broad routes to sourcing RLHF capacity, and they differ sharply on quality, cost and control.
- Per-hour gig platforms (for example Mercor, Surge, Scale). You tap a marketplace of individual annotators or experts billed per hour or per task. Fast to start, large talent pools, but typically expensive at scale and with the management burden on you.
- Build it in-house. Maximum control and IP retention, but you carry recruitment, training, tooling, QA and management, which is slow and costly to stand up.
- Managed RLHF pods (for example OSCABE). A dedicated, managed team is recruited, trained and run for you under one contract. You get the consistency of an in-house team without the overhead.
OSCABE provides managed RLHF pods through its AI Training Teams service. The differentiator is "Trained First": your pod is trained on your own rubric and workflow before it produces a single label, so calibration is built in from day one rather than learned through expensive trial and error.
Indicative OSCABE pricing is transparent and monthly:
| OSCABE managed pod | From (per month) | Typical use |
|---|---|---|
| Coding RLHF Team | £6,000 | Code review and coding RLHF |
| Training Data Pipeline Team | £8,000 | Annotation and data pipelines |
| Domain Expert AI Team | £9,000 | Legal, medical, finance, STEM evaluation |
| RLHF Evaluation Team | £10,000 | Preference data, model evaluation, red-teaming |
That sits roughly 75 to 80% below the effective cost of sourcing equivalent expert hours on per-hour gig platforms, with management and a UK contract included. The talent is drawn from India and the Middle East, including CE-verified engineers, ICAI chartered accountants and IIT/NIT-trained ML and software experts.
What makes an RLHF team good rather than cheap?
The cheapest annotation is usually the most expensive long term, because inconsistent labels degrade your reward model and you pay to redo the work. Three things separate a strong RLHF team:
- Calibration: every annotator interprets the rubric the same way, measured by inter-annotator agreement.
- Domain fit: the people judging legal or clinical outputs are actually qualified to do so.
- Feedback loops: disagreements are adjudicated and the rubric is refined, not ignored.
A managed pod is built around exactly these controls. Because the same dedicated people work your project month after month, calibration compounds instead of resetting with every new freelancer. See how the staffing model works on how it works and explore wider managed teams options.
Frequently asked questions
What is the difference between RLHF and fine-tuning?
Fine-tuning is the broad practice of further training a model on specific data. Supervised fine-tuning (SFT) is one type, where the model imitates human-written examples. RLHF goes a step further: after SFT, it uses human preference rankings and reinforcement learning to optimise the model toward outputs humans prefer. RLHF almost always includes an SFT stage as its first step.
Is RLHF still needed if I use DPO?
Yes, in the sense that you still need human preference data. DPO replaces the separate reward model and PPO loop with a simpler direct optimisation, but it is still trained on human comparisons between responses. The human-feedback bottleneck remains, which is why a trained, consistent annotation team matters regardless of the algorithm.
Who should write SFT and preference data for technical domains?
People qualified in the domain. Coding RLHF should be done by engineers who can actually judge whether code is correct and idiomatic; medical or legal evaluation needs certified professionals. OSCABE staffs domain-expert pods (CE-verified engineers, ICAI chartered accountants, IIT/NIT-trained experts) precisely so the human judgments behind your model are trustworthy. See our guide to hiring domain experts for AI model evaluation.
How much does an RLHF team cost?
On per-hour platforms, expert annotation can run very high once you account for management overhead and rework. OSCABE managed pods start from £6,000 per month for a Coding RLHF Team and £10,000 per month for an RLHF Evaluation Team, roughly 75 to 80% cheaper than gig-platform equivalents, with management and a UK contract included. See full pricing for details.
Build a better-aligned model with a managed RLHF pod
RLHF is only as good as the humans behind it. If you want consistent, domain-qualified feedback without the cost and overhead of standing up an annotation operation yourself, a managed pod is the pragmatic path. For a fair look at the alternatives, read our comparison of Mercor vs Surge vs OSCABE for AI training.
When you are ready to put real people behind your reward model, explore OSCABE's AI Training Teams or talk to us. We will scope an RLHF pod trained on your rubric, with transparent monthly pricing and a UK contract, so the feedback that shapes your model is consistent from the first label.