Why LLM Post-Training Lacks Ready-Made Data Commons

Because LLM post-training consumes structured tasks paired with executable environments rather than static documents, it cannot directly leverage the internet's existing knowledge commons, leading to a severe data shortage. Taking software engineering as an example, even when building verifiable SWE-bench-style tasks from open-source code repositories, the qualification and execution rates remain extremely low—and turning real-world workflows from other industries into executable tasks is even harder. The author calls on the open-source community to systematically build public post-training environments and task commons to prevent frontier agent training capabilities from being monopolized by a handful of closed-source tech giants.