SkyRL tx - A Unified Training and Inference Engine

Explore a groundbreaking conference talk that introduces SkyRL tx, an open-source system revolutionizing transformer model operations by unifying training and inference through a single REST-based interface. Learn how this innovative approach treats post-training as an extension of the inference engine itself, supporting both forward and backward passes through the same unified interface and eliminating the complexity of maintaining separate training and inference stacks. Discover how SkyRL tx leverages LoRA-based adapters to enable cost-efficient multi-tenancy, allowing multiple users to share a common base model while customizing behavior through lightweight, isolated adapters. Understand the core architecture and implementation details, key design decisions behind the unified approach, and how the system achieves compatibility with Thinking Machines' Tinker API. Gain insights into the project's roadmap, upcoming capabilities, extensibility points, and opportunities for community contribution and adoption. Perfect for researchers and developers interested in experimenting with unified model execution patterns, as well as organizations looking to run their own Tinker-compatible backend on self-managed hardware while simplifying model operations, reducing infrastructure overhead, and opening new avenues for scalable, flexible transformer development.