Pretraining large language models benefited from a vast, open digital commons—including Wikipedia, Common Crawl, and Project Gutenberg—providing ready-made text data. However, the shift to posttraining, especially for agentic AI, faces a critical bottleneck: there is no equivalent public repository of task-based data. Unlike pretraining, which consumes documents, posttraining relies on reinforcement learning tasks that require simulated environments, intents, tools, and graders.
These tasks are inherently ephemeral and context-dependent, making them difficult to archive or standardize. While open models like Nemotron still depend on web-derived data for pretraining, the lack of a shared posttraining commons threatens the future of truly open AI development. Without collaborative infrastructure for task data, the field risks fragmentation and closed proprietary systems.
Source: Hacker News · Summarized by HeadlinesBriefing