W&B Training by Weights & Biases
The fast and easy way to train AI agents with serverless RL
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Dave Davies

W&B Training by Weights & Biases - The fast and easy way to train AI agents with serverless RL

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

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Dave Davies
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Training reliable, multi-turn AI agents with reinforcement learning has always been complex, unstable, and expensive. W&B Training changes that. With serverless RL, ART (Agent Reinforcement Trainer), and RULER (Relative Universal LLM-Elicited Rewards), you can fine-tune large language models to become more capable and trustworthy without managing infrastructure or writing custom reward functions. Here’s what you can do with W&B Training: - Run RL fine-tuning loops in minutes — no GPUs or infra setup needed - Use ART, an open-source RL framework, to train agents faster and more stably - Replace reward engineering with RULER, an LLM-as-a-judge verifier - 1.4x faster training at 40% lower cost with CoreWeave’s optimized GPU packing - Get built-in observability to monitor rewards, rollouts, and convergence - Achieve faster, cheaper, and more reliable training — all from your W&B workspace Whether you’re building reasoning agents, copilots, or evaluators, W&B Training gives you a production-ready RL stack that’s fast, scalable, and simple. Try it out → https://wandb.ai/site/wb-training/