Bespoke Labs raises $40M to automate AI model training

Post-training has emerged as a critical bottleneck in AI development because it directly shapes how well a deployed model performs on real-world tasks. The body describes two main post-training methods: reinforcement learning, which trains models on sample tasks in simulated environments, and supervised fine-tuning, which feeds models example prompts and answers. Both are labor-intensive; reinforcement learning requires building custom sandboxes for each use case (a productivity agent needs different simulations than a coding agent), and supervised fine-tuning requires assembling large datasets of question-answer pairs—a process the body calls "highly time-consuming."

Bespoke's approach targets both bottlenecks. Its platform automates the creation of training environments using automation workflows and human expertise, reducing the manual engineering overhead. It then applies automated optimization (via tools like GEPA) to improve model output without requiring engineers to manually experiment with every prompt variation. The funding, which includes backing from a major AI lab (Google DeepMind) and established venture firms, signals confidence that tooling for post-training—once considered routine infrastructure—can now be a standalone venture with substantial investor interest. Bespoke's open-source releases (GEPA and OpenThoughts) appear designed both to build adoption and to validate that its automation approach yields faster, better results than existing manual and dataset-based alternatives.