Principle:OpenRLHF OpenRLHF PPO Training Loop

Knowledge Sources	Proximal Policy Optimization Algorithms Training language models to follow instructions
Domains	Reinforcement_Learning, Training
Last Updated	2026-02-07 00:00 GMT

Overview

A multi-model training orchestrator that coordinates on-policy generation, reward scoring, advantage estimation, and policy/value function updates in the PPO-RLHF loop.

Description

PPO Training Loop orchestrates the complex interaction between multiple models in RLHF:

1. Generation: vLLM generates responses from prompts using the current policy
2. Scoring: Reference model and reward model score the generated responses
3. Experience Making: KL penalties, advantages (GAE), and returns are computed
4. Training: Actor (policy) and Critic (value function) are updated using PPO objectives
5. Weight Sync: Updated policy weights are broadcast to vLLM engines

This cycle repeats for each batch of prompts.

Usage

Use for PPO-based RLHF training with a trained reward model, or for GRPO with rule-based rewards (no critic). Requires Ray cluster with multiple GPU groups.

Theoretical Basis

PPO-RLHF combines:

• On-policy generation: Fresh samples from current policy
• GAE (Generalized Advantage Estimation): $A_t=\sum _{l=0}^{\mathrm{\infty }}(\gamma \lambda {)}^l{\delta }_{t+l}$
• Clipped policy gradient: Conservative actor updates
• Clipped value function: Stable critic updates
• KL penalty: Prevents excessive divergence from reference

Related Pages

Implemented By

• Implementation:OpenRLHF_OpenRLHF_PPOTrainer_fit