Verified claim · AI-ML · 92% confidence
Odds Ratio Preference Optimization (ORPO) introduced in paper: ORPO: Monolithic Preference Optimization without Reference Model (Hong et al., 2024).
Last verified 2026-05-31 · Methodology veritas-v0.1 · ff0975d391b66a6f
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Structured fields
- Subject
- Odds Ratio Preference Optimization (ORPO)
- Predicate
introduced_in_paper- Object
- ORPO: Monolithic Preference Optimization without Reference Model (Hong et al., 2024)
- Confidence
- 92%
- Tags
- orpo · odds-ratio-preference-optimization · alignment · preference-optimization · rlhf · hong · 2024
Sources (3)
[1] preprint · arXiv (Hong, Lee, Thorne — KAIST AI) · 2024-03-12
ORPO: Monolithic Preference Optimization without Reference Model“we introduce a straightforward and innovative reference model-free monolithic odds ratio preference optimization algorithm, ORPO”
[2] github release · KAIST AI (xfactlab) · 2024-03-12
ORPO official repository[3] docs · Hugging Face
ORPO (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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Evidence comes from 3 primary sources: arXiv (Hong, Lee, Thorne — KAIST AI), KAIST AI (xfactlab), Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/ff0975d391b66a6f.json includes an HMAC-SHA256 signature for audit verification.
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// "Odds Ratio Preference Optimization (ORPO) introduced in paper: ORPO: Monolithic Preference Optimization without Reference Model (Hong et al., 2024)."Python
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# "Odds Ratio Preference Optimization (ORPO) introduced in paper: ORPO: Monolithic Preference Optimization without Reference Model (Hong et al., 2024)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_odds_ratio_preference_optimization_orpo_fact() -> dict:
"""Fetch the verified SourceScore claim for Odds Ratio Preference Optimization (ORPO)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/ff0975d391b66a6f.json")
return r.json()