---
title: 'Deep reinforcement learning from human preferences'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/1706-03741-rl-from-human-preferences.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei, Deep reinforcement learning from human preferences (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: foundations'
tags: arxiv, rlhf, preferences, foundations
arxiv_id: 1706.03741
alias: 1706.03741-rl-from-human-preferences
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Deep reinforcement learning from human preferences'
    publisher: arXiv
    authors: 'Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei'
    url: https://arxiv.org/abs/1706.03741
    version_read: 'v4 (17 Feb 2023)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Deep reinforcement learning from human preferences

## Summary
[Deep reinforcement learning from human preferences](https://arxiv.org/abs/1706.03741) (2017) introduced the method of training a reward model from human comparisons, then optimizing a policy against that reward. It was first demonstrated on simulated control tasks, years before it became the standard way to align language-model assistants.

What it introduced: preference-based reward learning plus reinforcement learning on the resulting reward model, so that humans can steer a system by comparing behaviors instead of writing a scalar reward by hand.

What it led to: InstructGPT and the later RLHF stack for chat models, and then alternatives such as direct preference optimization. Live notes: [2204-05862-helpful-harmless-rlhf](https://iinventedlight.com/library/notes/2204-05862-helpful-harmless-rlhf/) and [2305-18290-direct-preference-optimization](https://iinventedlight.com/library/notes/2305-18290-direct-preference-optimization/). See also the PPO pointer in this batch, which became the usual policy-gradient algorithm in that stack.

Where to read it: https://arxiv.org/abs/1706.03741 (v4, last revised 17 Feb 2023).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).