Reinforcement Learning | CoreWeave Solutions

Reinforcement learning for agents, from demo to done

Ship reliable agents with low latency and cost efficiency for your use case. CoreWeave Cloud provides RL as a service, including serverless infrastructure, frameworks, and APIs.

Reinforcement learning
as a service

Reinforcement learning (RL) determines whether an AI model ships to production or stays a demo. Yet RL remains hard to adopt—GPU capacity is scarce, orchestration is complex, and most teams lack the resources to build and maintain custom RL stacks. CoreWeave removes that friction with RL-as-a-service, delivering flexible, managed GPU infrastructure—serverless or provisioned—so your team can train, iterate, and ship reliable agents faster.

Put reliable agents into production with confidence

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Zero infrastructure headaches

CoreWeave Cloud gives you as-a-service access to powerful GPU capacity for RL jobs, with automatic scaling. We manage and maintain the infrastructure so your RL jobs stay resilient, letting you focus on innovation, not infrastructure.

1.4x faster training

Serverless RL speeds up training by around 1.4× with no quality loss. Training and inference run on separate always-on CoreWeave instances, so edits to rollout or loop apply in seconds, not minutes.

40% lower cost than self-managed

Serverless RL packs jobs to maximize utilization, cutting costs up to 40%. Rollouts are run on a shared GPU cluster with per-token billing, helping you keep costs low.

Control the RL while we handle the infrastructure

Post-train LLMs for multi-turn agentic tasks to improve reliability, speed, and costs without managing infrastructure. Your teams can maintain control over key aspects of RL, we’ll take care of distributed training and inference.

CoreWeave Cloud platform for
reinforcement learning

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GPU Compute

Run distributed workloads with predictable performance and full control as your experiments scale into production. Purpose-built cloud infrastructure for serving and running AI models, CoreWeave Cloud provides bare metal access to the latest architectures.

CoreWeave Mission Control

Monitor training runs, diagnose issues, and manage large-scale infrastructure with confidence. The operating standard for running AI on CoreWeave Cloud, CoreWeave Mission Control provides unified visibility into GPU, network, and storage health.

SUNK (Slurm on Kubernetes)

Run distributed workloads efficiently, isolating failures and managing GPU resources across complex research environments. SUNK is an AI-native research cluster designed for large-scale, distributed model training, combining Slurm scheduling with Kubernetes orchestration.

CKS (CoreWeave Kubernetes Service)

Reduce overhead while preserving flexibility with preconfigured clusters, high-performance networking and storage, and managed operations. CKS is a managed Kubernetes service optimized for AI workloads to provide a cloud-native environment for distributed training and experimentation.

CoreWeave AI Object Storage

Simplify data management and ensure consistent access to large-scale training data throughout the model lifecycle. A high-performance object storage system built for AI training pipelines, CoreWeave AI Object Storage provides a single, global dataset accessible across clusters.

Explore the CoreWeave Cloud Platform

The easiest and fastest way to train AI agents with RL

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CoreWeave moves fast—constantly expanding the platform with new capabilities. The acquisition of Weights & Biases brings best-in-class AI development tools directly into our stack, empowering researchers and engineers to develop AI agents and models. Trusted by over 1,500 teams, including 30+ foundation model builders, Weights & Biases helps AI teams iterate faster to deliver real-world impact.

With Weights & Biases, you can:

Pre-train and post-train LLMs for agentic tasks
Evaluate, iterate, monitor, and safeguard agents
Tap into enterprise-grade performance, scale, governance, and security

W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs.

Serverless RL

Serverless RL lets you post-train LLMs for multi-turn agentic tasks to improve reliability, speed, and costs without provisioning and managing infrastructure.

Agent Reinforcement Trainer (ART)

Agent Reinforcement Trainer (ART) is an open-source framework that implements the RL algorithm to post-train agentic LLMs and improve their reliability.

RULER

Relative Universal LLM-Elicited Rewards (RULER) is a general-purpose RL reward function that reliably improves agent performance without the need for labeled data or handcrafted reward functions. Just define your task in a text prompt, and RULER handles the rest.

The world’s leading AI pioneers trust CoreWeave

Frequently Asked Questions

What makes Serverless RL unique?

Serverless RL is the first publicly available service for flexibly training models with reinforcement learning. Serverless RL does the hard work of managing your training and inference infrastructure, letting you focus on the key tasks of defining your data, environment and reward function. This leads to faster feedback cycles, lower costs, and far less time on DevOps.

How do I serve the fine-tuned model?

Serverless RL integrates with W&B Inference. After training a Low Rank Adapter (LoRA), it is automatically stored in W&B Registry and loaded into W&B Inference on every request. Just reference the LoRA in your OpenAI-like API call to W&B Inference, and we will load the correct LoRA on top of the base model and return the response. If you prefer, you can also download the LoRA and deploy it in your own environment or on a third-party inference service. Learn more.

How does Serverless RL solve the “straggler problem” to deliver faster, cheaper training without quality loss?

At the start of each RL step, GPU utilization is high because many rollouts run in parallel, and it is also high during the training phase. The problem is the long middle period where utilization drops while you wait for a small number of slow rollouts to finish. This is the straggler problem. Serverless RL addresses it by multiplexing many training jobs onto a shared W&B Inference cluster during rollouts, keeping utilization high in aggregate. You pay only for the incremental tokens generated, which can significantly cut costs, for example 40% lower cost on our benchmark (ART-E agent) with no loss in model quality.

What are the ideal use cases for Serverless RL?

Serverless RL is ideal for voice agents, customer support, deep research, and agentic RAG. If you already have an agent but it does not yet meet production requirements for reliability or latency, Serverless RL helps you get there quickly.

How is Serverless RL priced?

Serverless RL splits the rollout inference and distributed training workloads of the RL loop and runs them on separate CoreWeave GPU clusters to maximize utilization and minimize cost. You pay only for active usage, not idle time. Pricing has three components: inference, training, and storage. For details, see the Serverless RL pricing page.

On-demand webinar

Introducing serverless reinforcement learning

Train reliable AI agents without the hassle of managing GPUs or infrastructure. In this on-demand webinar, we introduce Serverless Reinforcement Learning, a fully managed approach that lets teams fine-tune agents for reliability, speed, and cost in minutes. CoreWeave delivers instant access, elastic scaling, and production-ready performance without the usual setup overhead.

Watch on demand