> For the complete documentation index, see [llms.txt](https://docs.folio.works/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.folio.works/learn/rlhf.md).

# rlhf

**RLHF** is the primary technique used to align large language models (LLMs) with human values, preferences, and safety standards. It is how companies like OpenAI, Anthropic, and Google fine-tune their models based on human judgment rather than automated metrics alone.

## What is RLHF?

In a standard RLHF workflow:

1. A base AI model generates multiple candidate responses to a prompt.
2. Human trainers review those responses and rank or select the best one.
3. A **reward model** is trained on those human preferences.
4. The base model is updated using reinforcement learning to produce responses the reward model — and therefore humans — would rate highly.

The result is a model that is better calibrated to human expectations: more helpful, more accurate, and safer.

## How Trainers Contribute

As a Folio trainer participating in RLHF projects, your primary task is **preference annotation** — reviewing pairs or sets of AI responses and indicating which is better, and why.

Typical RLHF tasks include:

* **Response ranking** — Given two or more AI outputs, select the best response based on accuracy, helpfulness, and safety
* **Response rating** — Score a single response on defined criteria (e.g., 1–5 scale for medical accuracy)
* **Rationale writing** — Provide a brief written explanation for your ranking or rating
* **Failure identification** — Flag responses that are factually wrong, harmful, or incomplete

In healthcare AI contexts, your clinical expertise directly shapes the reward signal that trains the model. A physician ranking clinical summaries is providing information that no automated system can replicate.

## Why RLHF Matters in Healthcare

Healthcare AI systems carry high stakes. A model used for clinical decision support, patient education, or diagnostic assistance must produce accurate, safe responses. RLHF with domain-expert annotators is the primary mechanism for achieving that standard.

Without expert human feedback, models may:

* Produce responses that sound medically plausible but are factually incorrect
* Omit critical safety warnings
* Fail to recognize rare but serious conditions
* Prioritize confident-sounding language over accuracy

Healthcare professionals on Folio are uniquely qualified to catch these failure modes.

## Course: RLHF 1

| Detail       | Info       |
| ------------ | ---------- |
| **Duration** | 15 minutes |
| **Lessons**  | 3          |
| **Format**   | Self-paced |

**RLHF 1** covers the foundational concepts of reinforcement learning from human feedback, explains how trainers fit into the RLHF pipeline, and walks through practical examples of preference annotation tasks.

### To access RLHF 1:

1. Go to **Learn** in the left sidebar.
2. Scroll to the **RLHF** section.
3. Click **Start Lesson** on the RLHF 1 card.