Oat

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OAT is an AI Frameworks & Infra tool that provides a research-friendly framework for LLM online alignment. It supports reinforcement learning, preference learning, and online exploration algorithms.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is oat?

OAT (Online Alignment Toolkit) is a simple yet efficient open-source framework designed for running online LLM alignment algorithms. It features a distributed Actor-Learner-Oracle architecture optimized for high efficiency, utilizing vLLM for accelerated response sampling and DeepSpeed ZeRO for memory efficiency. OAT simplifies the experimental pipeline by providing an online Oracle for preference data labeling and real-time model evaluation. Researchers can simulate various feedback types, including verifiable rewards and LLM-as-a-judge, with flexible deployment options for reward models. Its modular structure facilitates rapid prototyping and experimentation, implementing cutting-edge algorithms like PPO/Dr.GRPO for online RL and Online DPO/SimPO/IPO for preference learning, fostering innovation and fair benchmarking.

Best used for

Ideal for researchers and machine learning engineers who need to develop and experiment with online LLM alignment algorithms, conduct reinforcement learning, and perform preference learning. Especially valuable for those requiring a high-efficiency, modular framework with robust Oracle simulation capabilities for rapid prototyping.

Common actions

align LLMs

experiment with reinforcement learning

conduct preference learning

simulate online feedback

prototype AI algorithms

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Capabilities

Key features

Distributed Actor-Learner-Oracle architecture
vLLM for accelerated sampling
DeepSpeed ZeRO for memory
Oracle simulation for feedback
Modular structure
PPO/Dr.GRPO implementation
Online DPO/SimPO/IPO

Target Audience

professormachine learning engineerresearcher

Integrations

wandbvllmdeepspeedmosec

Pricing & Plans

Open Source

Free

FAQs

What kind of LLM alignment algorithms does OAT support?

OAT supports a range of online LLM alignment algorithms, including reinforcement learning methods like PPO/Dr.GRPO, preference learning algorithms such as Online DPO/SimPO/IPO, and online exploration (active alignment) algorithms like SEA, APL, and XPO.

How does OAT achieve high efficiency for LLM alignment?

OAT utilizes a distributed Actor-Learner-Oracle architecture. The Actor component uses vLLM for accelerated online response sampling, while the Learner leverages DeepSpeed ZeRO strategies to enhance memory efficiency, optimizing the overall alignment process.

Can OAT simulate different types of feedback for LLM training?

Yes, OAT provides a diverse set of oracles to simulate various feedback types, including preference, reward, and verification feedback. It supports verifiable rewards using rule-based functions, lightweight reward models, and LLM-as-a-judge via OpenAI API for model-based pairwise ranking.

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Research & Education › Academic Research Coding & Development › Open Source & Models Research & Education › Scientific Computing

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