OpenRLHF

What it is

OpenRLHF is an easy-to-use, scalable reinforcement learning with human feedback (RLHF) framework that supports full tuning of models up to 70B parameters. It includes features like LoRA, RingAttention, RFT, and iterative DPO for high-performance training.

OpenRLHF stands out as an open-source, scalable RLHF framework with a focus on high-performance tuning and iterative DPO, making it ideal for large-scale reinforcement learning projects.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited documentation and examples for advanced features like LoRA, RingAttention, RFT

   Official docs lack detailed guides on implementing these optimizations effectively

2. 02

   Performance degradation with models larger than 10B parameters due to memory constraints

   Internal benchmarks show significant slowdowns and out-of-memory errors for models over 10B parameters

What it does well

• Supports full tuning of models up to 70B parameters

• Includes iterative DPO for high-performance training

• Features LoRA, RingAttention, and RFT optimizations

• Easy-to-use with a focus on scalability

• Open-source under Apache-2.0 license

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.