---
schema: 1
type: term
title: RLHF
description: >-
  **RLHF (reinforcement learning from human feedback)** is a tuning step applied
  to a language model after its base training, shaping the raw text-continuation
  engine into an assistant that tries hard to align with and follow the
  instructions you give it.
canonical: 'https://liquidgold.cc/channel/everyinc/explore/term/rlhf-8e9ec22d2733'
markdown: 'https://liquidgold.cc/channel/everyinc/explore/term/rlhf-8e9ec22d2733.md'
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# RLHF

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/rlhf-8e9ec22d2733

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**RLHF (reinforcement learning from human feedback)** is a tuning step applied to a language model after its base training, shaping the raw text-continuation engine into an assistant that tries hard to align with and follow the instructions you give it.

In the captured conversations, **RLHF (reinforcement learning from human feedback)** appears as the tuning step that shapes how a model responds to your instructions — and it shows up through two of its practical consequences rather than as a formal definition.Linus Lee warns that RLHF-tuned models try hard to align with whatever you specify. In his Bangkok travel prompt, merely mentioning one specific park caused the model to over-index on it in every single suggestion. His words:> I'm always a little bit afraid of any kind of instructions over steering the model.The clearest contrast comes from Lee's personal **iMessage chatbot**, which runs on the *raw base* Llama 2 13B — before fine-tuning or RLHF. A base model is a pure text-continuation engine: ask it a question directly and it may just continue writing a blog post around the question. Yet with prompting alone it talks like “a fairly intelligent and cogent rando on the internet,” giving opinions — exactly what Lee wants. RLHF is what turns that raw continuation engine into the assistant-like behavior you see in ChatGPT-style tools.- RLHF is a tuning step applied to a base model, shaping it into an assistant that follows instructions.- RLHF-tuned models **over-steer**: they over-index on any specific detail you mention, so watch how specific your instructions are.- A model *before* RLHF is a text-continuation engine; with the right prompts it can still give the personal, opinionated answers some applications want.