OpenLMLab/MOSS-RLHF

Health v2 · maintenance only

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1429 stars · 103 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MOSS-RLHF is the open-source companion code for the paper 'Secrets of RLHF in Large Language Models Part I: PPO', providing implementations of PPO-based RLHF training and reward model training for large language models. It also releases 7B English and Chinese reward models, SFT and policy models, and a preference-strength-annotated HH-RLHF dataset.

Use cases

• train an LLM with PPO-based RLHF
• train a reward model from human preference data
• reproduce RLHF alignment experiments from the paper
• download pretrained 7B reward and policy models
• use a cleaned HH-RLHF dataset with preference strength labels
• study reward model strength measurement for alignment research

When to choose

• you want a research-grade reference implementation of PPO for LLM alignment
• you need reward model training code with an annotated preference dataset
• you are studying RLHF mechanics or writing an alignment paper
• you want 7B English/Chinese reward models to score LLM outputs

When to avoid

• you need a production-ready, actively maintained RLHF training framework
• you want scalable multi-node training with the latest algorithm variants like GRPO or DPO out of the box
• you need commercial use of the released models (model weights are AGPL-3.0, data is CC BY-NC 4.0)
• you expect frequent updates - the last release was March 2024

Facets

library · maturity maintenance

llm-training machine-learning deep-learning rag large-language-models machine-learning deep-learning artificial-intelligence python rlhf ppo reward-model alignment ai-safety research-code llm-alignment gpu linux

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