Verified claim · AI-ML · 100% confidence
Proximal Policy Optimization (PPO) introduced in paper: Proximal Policy Optimization Algorithms (Schulman et al., 2017).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 00f224e1ccc158ef
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Structured fields
- Subject
- Proximal Policy Optimization (PPO)
- Predicate
introduced_in_paper- Object
- Proximal Policy Optimization Algorithms (Schulman et al., 2017)
- Confidence
- 100%
- Tags
- ppo · reinforcement-learning · foundational · schulman · 2017 · openai · rlhf
Sources (2)
[1] preprint · arXiv (Schulman, Wolski, Dhariwal, Radford, Klimov) · 2017-07-20
Proximal Policy Optimization Algorithms“We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.”
[2] official blog · OpenAI · 2017-07-20
Proximal Policy OptimizationOpenAI is rated by SourceScore — see its reliability →
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cURL
curl https://sourcescore.org/api/v1/claims/00f224e1ccc158ef.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/00f224e1ccc158ef.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Proximal Policy Optimization (PPO) introduced in paper: Proximal Policy Optimization Algorithms (Schulman et al., 2017)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/00f224e1ccc158ef.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Proximal Policy Optimization (PPO) introduced in paper: Proximal Policy Optimization Algorithms (Schulman et al., 2017)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_proximal_policy_optimization_ppo_fact() -> dict:
"""Fetch the verified SourceScore claim for Proximal Policy Optimization (PPO)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/00f224e1ccc158ef.json")
return r.json()