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Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017).

Last verified 2026-05-16 · Methodology veritas-v0.1 · 67866330cd60e54d

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Structured fields

Subject
Reinforcement Learning from Human Feedback (RLHF)
Predicate
introduced_in_paper
Object
Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017)
Confidence
100%
Tags
rlhf · alignment · foundational · christiano · 2017 · nips

Sources (3)

  1. [1] preprint · arXiv (Christiano, Leike, Brown, Martic, Legg, Amodei) · 2017-06-12

    Deep Reinforcement Learning from Human Preferences
    “For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. … We explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments.”
  2. [2] peer reviewed · NeurIPS Foundation · 2017-12-04

    Deep RL from Human Preferences (NeurIPS 2017 proceedings)
  3. [3] official blog · OpenAI · 2017-06-13

    Learning from human preferencesOpenAI is rated by SourceScore — see its reliability →

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Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017). — SourceScore Claim 67866330cd60e54d (verified 2026-05-16). https://sourcescore.org/api/v1/claims/67866330cd60e54d.json

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The record lists 3 cited source(s): arXiv (Christiano, Leike, Brown, Martic, Legg, Amodei), NeurIPS Foundation, OpenAI. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/67866330cd60e54d.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.

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const r = await fetch("https://sourcescore.org/api/v1/claims/67866330cd60e54d.json"); const envelope = await r.json(); console.log(envelope.claim.statement); // "Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017)."

Python

import httpx r = httpx.get("https://sourcescore.org/api/v1/claims/67866330cd60e54d.json") envelope = r.json() print(envelope["claim"]["statement"]) # "Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017)."

LangChain (retrieve-then-cite)

from langchain_core.tools import tool import httpx @tool def get_reinforcement_learning_from_human_feedback_rlhf_fact() -> dict: """Fetch the verified SourceScore claim for Reinforcement Learning from Human Feedback (RLHF).""" r = httpx.get("https://sourcescore.org/api/v1/claims/67866330cd60e54d.json") return r.json()
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