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.