CoreWeave Arena: Serverless inference and serverless RL
CoreWeave Arena provides a platform for validating AI workloads end-to-end—including pre-training, building agents, and post-training via Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)—before committing to production. The presentation demonstrates how Serverless Inference and Serverless RL can be used to build and improve AI agents, specifically showing how RL can significantly boost agent performance and reliability, which is then deployed using CoreWeave Serverless Inference.
Key takeaways
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CoreWeave Arena Purpose
Arena is designed for validating AI workloads under production-like conditions, enabling users to run real workloads on CoreWeave and pair it with Coreweave Forge to close the AI development loop.
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Agent Development & Tracing
2:00
Agents can be built using models like Quen 314b instruct via Serverless Inference. Performance is monitored by collecting agent traces and using custom signals (e.g., misalignment between user request and generated SQL statement) to identify behavioral issues.
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Serverless RL for Improvement
2:50
Serverless RL is presented as a preferred method for improving AI application reliability by fine-tuning LLMs for agentic tasks. Coreweave addresses the challenges of RL by providing instant, elastic GPU capacity without provisioning.
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Evaluation and Deployment
4:20
Agent improvement is validated using pre- and post-RL agent evaluations (e.g., spider charts). Once optimized, the model weights and necessary Python code for calling the model via Coreweave Serverless Inference are retrieved for production deployment.