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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.

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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.

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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.

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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.

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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.

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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.