There is no posttraining data commons (openathena.ai)

Recent discussions highlight a significant gap in the AI/ML landscape: the absence of a posttraining data commons, a counterpart to the well-established pretraining data commons that has fueled the development of large language models (LLMs). While pretraining relied on vast, readily available data from resources like Wikipedia, Common Crawl, and various open-source repositories, posttraining requires specific task-oriented data that is less accessible. This shift from consuming documents to tasks presents a unique challenge, as human tasks are often ephemeral and context-dependent, making them difficult to catalog and standardize for reinforcement learning (RL) systems. The implications for the AI community are profound. As the frontier of AI research pivots towards more complex "agentic" AI, the need for systematic methods to inscribe human activities into actionable RL tasks becomes critical. Current reliance on existing software repositories has shown that translating tasks into a form suitable for RL is fraught with difficulties, with many tasks being poorly defined or non-executable. To address these challenges, researchers may need to collaborate with professionals from various fields—such as law and medicine—to formalize their activities into reusable task frameworks. This necessity raises the stakes in developing a robust posttraining commons, which, if achieved, could democratize access to valuable AI training data and empower a wider range of AI applications.