---
title: "Proximal Policy Optimization Algorithms"
description: "Proximal Policy Optimization shows how to reuse on-policy experience for several minibatch updates without letting the policy move too far in one step. Read it to understand the clipped surrogate objective behind a practical reinforcement-learning algorithm that later became central to RLHF pipelines."
canonical_url: "https://fanout.sh/daily/2026-08-01-proximal-policy-optimization"
md_url: "https://fanout.sh/daily/2026-08-01-proximal-policy-optimization.md"
last_updated: "2026-08-01"
access: "public"
---

# Proximal Policy Optimization Algorithms

Proximal Policy Optimization shows how to reuse on-policy experience for several minibatch updates without letting the policy move too far in one step. Read it to understand the clipped surrogate objective behind a practical reinforcement-learning algorithm that later became central to RLHF pipelines.

## Paper details

- Authors: Schulman et al.

- Venue: arXiv 2017

- Track: ML Math

- Difficulty: Hard

- Reading time: 55 min

- Original paper: https://arxiv.org/abs/1707.06347

## What you will learn

- How the probability ratio compares the new policy with the policy that collected each action.

- Why clipping that ratio limits the incentive for destructively large policy updates while keeping the objective simple to optimize.

- How multiple epochs of minibatch updates improve sample use, and why PPO balances empirical performance, implementation simplicity, and wall-clock cost.

## Continue exploring

[Explore PPO](https://fanout.sh/knowledge-graph?node=ai-research%3Aconcept%3Appo): Open Fanout's PPO node and connect the paper to policy gradients, importance ratios, clipped objectives, advantage estimates, and RLHF.

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