Verified claim · AI-ML · 92% confidence
Simple Preference Optimization (SimPO) introduced in paper: SimPO: Simple Preference Optimization with a Reference-Free Reward (Meng et al., 2024).
Last verified 2026-05-31 · Methodology veritas-v0.1 · d47e9b204e1e73bd
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Structured fields
- Subject
- Simple Preference Optimization (SimPO)
- Predicate
introduced_in_paper- Object
- SimPO: Simple Preference Optimization with a Reference-Free Reward (Meng et al., 2024)
- Confidence
- 92%
- Tags
- simpo · simple-preference-optimization · alignment · preference-optimization · reference-free · meng · 2024
Sources (3)
[1] preprint · arXiv (Meng, Xia, Chen — University of Virginia + Princeton) · 2024-05-23
SimPO: Simple Preference Optimization with a Reference-Free Reward“The effectiveness of SimPO is attributed to a key design: using the average log probability of a sequence as the implicit reward.”
[2] github release · Princeton NLP (princeton-nlp) · 2024-05-23
SimPO official repository (NeurIPS 2024)[3] docs · Hugging Face
SimPO (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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// "Simple Preference Optimization (SimPO) introduced in paper: SimPO: Simple Preference Optimization with a Reference-Free Reward (Meng et al., 2024)."Python
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# "Simple Preference Optimization (SimPO) introduced in paper: SimPO: Simple Preference Optimization with a Reference-Free Reward (Meng et al., 2024)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_simple_preference_optimization_simpo_fact() -> dict:
"""Fetch the verified SourceScore claim for Simple Preference Optimization (SimPO)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/d47e9b204e1e73bd.json")
return r.json()