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Tyler Griggs

Philipp Moritz

Explore a technical comparison of leading Reinforcement Learning (RL) libraries for LLMs from Ray. This guide analyzes frameworks like TRL, Verl, and RAGEN to help developers choose the best tools for RLHF, reasoning, and agentic AI.

The architecture of a Reinforcement Learning (RL) library is split into two primary components: Generation and Training. During the generation phase, an LLM Engine performs multi-turn rollouts within an environment to produce data and reward signals. This output is then fed into the training phase to update the model's parameters. This process forms a feedback loop, where the progressively improved model generates the next iteration of data for continuous refinement.

Open Source RL Libraries for LLMs Graph

Open Source RL Libraries for LLMs

Weixin Astra Team

See how the Tencent Weixin team implemented Ray and Kubernetes to build ultra-large-scale distributed systems with Ray.

Figure 24: TFCC inference runtime

How Tencent’s Weixin AI Team Deployed Ray

Large-Scale Deployment of Ray in Tencent’s Weixin AI Infrastructure

Julian Forero

Unstructured data growth and GPU demands expose legacy compute limits. See where other frameworks fall short and how Ray fills the gap.

Your Data and AI Frameworks Evolved – What About Your Compute Framework? thumbnail

AI is Evolving – Can Your Compute Framework Keep Up?

Your Data and AI Frameworks Evolved – What About Your Distributed Compute Framework?

Kun Wu (Alibaba Cloud)

This blog was originally published by the Alibaba Cloud Infrastructure team.

KubeRay on ACK 

Ray on Alibaba Cloud: Building an ML Platform

Robert Nishihara

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Components of an Open Source AI Compute Tech Stack

An Open Source Stack for AI Compute: Kubernetes + Ray + PyTorch + vLLM

Kunling Geng

Building Scalable RAG Pipelines with Ray and Anyscale

Christina Zhu

The Ray Summit 2025 CfP is now open for submissions. The premier AI conference will be held November 3-5 in San Francisco, and the due date for CfPs is July 14th, 2025.  

Ray Summit 2025 CFP Open

Ray Summit 2025: Call for Proposals Closes on July 14th, 2025

Alexey Kudinkin

Praveen Gorthy

Richard Liaw

Richard Liaw headshot

Introducing: Hash-based shuffle backend on Ray Data, powering: joins and better performance for repartitioning and aggregations. Try it today.

Hash Shuffle / Joins blog header image

Anyscale Introduces Joins & Hash-Shuffle in Ray Data

New: Joins & Hash-Shuffle in Ray Data

Alan Guo

Cuong Nguyen

Justin Yu

Matthew Deng

Matthew Owen

To make debugging and performance tuning faster and more intuitive, we're excited to introduce Ray Train Dashboard and Ray Data Dashboard. Learn more.

Observability blog header image

Ray Train & Ray Data Dashboards on Anyscale

Streamline Distributed AI Monitoring and Debugging with New Ray Train & Ray Data Dashboards in Anyscale

Hao Chen

Learn how RayTurbo Data on Anyscale transforms how your teams work with large-scale data, dramatically reducing both processing times and operational risks.

RayTurbo Data blog header image

5x Faster Data Processing with RayTurbo Data

RayTurbo Data Improvements Deliver Up to 5x Faster Data Processing for AI Workloads

Dominic Catalano

Anyscale announces 3 new capabilities (AKS, GRS, and Multi-Region) giving you a comprehensive solution to get the most value from available AI compute. 

Cluster optimization blog header

Anyscale Announces Support for AKS, Multi-Cloud, & Multi-Region Deployments

Anyscale Adds First-Class Support for Azure Kubernetes Service and Streamlines Operations for Multi-Cloud & Multi-Region Deployments

Gabe Monroy, VP & GM Cloud Runtimes

Keerti Melkote, CEO of Anyscale

Google + Anyscale

Google Cloud Integrates Anyscale's RayTurbo with GKE

Simplifying AI Development at Scale: Google Cloud Integrates Anyscale's RayTurbo with GKE

At Ray Summit 2024, we brought together AI experts and practitioners to dive into the future of distributed AI and scalable machine learning.

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Ray Summit 2024: Breaking Through the AI Complexity Wall

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The Call for Speakers for Ray Summit 2025 has Been Extended!

Anyscale is the leading AI application platform. With Anyscale, developers can build, run and scale AI applications instantly.

Anyscale

Featured Posts and News

Ray Summit 2025: Call for Proposals Closes on July 14th, 2025

Simplifying AI Development at Scale: Google Cloud Integrates Anyscale's RayTurbo with GKE

Ray Summit 2024: Breaking Through the AI Complexity Wall

Open Source RL Libraries for LLMs

Large-Scale Deployment of Ray in Tencent’s Weixin AI Infrastructure

Your Data and AI Frameworks Evolved – What About Your Distributed Compute Framework?

Ray on Alibaba Cloud: Building an ML Platform

An Open Source Stack for AI Compute: Kubernetes + Ray + PyTorch + vLLM

Building Scalable RAG Pipelines with Ray and Anyscale

Ray Summit 2025: Call for Proposals Closes on July 14th, 2025

New: Joins & Hash-Shuffle in Ray Data

Streamline Distributed AI Monitoring and Debugging with New Ray Train & Ray Data Dashboards in Anyscale

RayTurbo Data Improvements Deliver Up to 5x Faster Data Processing for AI Workloads

Anyscale Adds First-Class Support for Azure Kubernetes Service and Streamlines Operations for Multi-Cloud & Multi-Region Deployments

Simplifying AI Development at Scale: Google Cloud Integrates Anyscale's RayTurbo with GKE