AutoDataBench examines whether AI agents can generate the verifiable agentic training tasks that currently depend on human labour to feed language model self-improvement loops. The benchmark targets the data-production step that supervised finetuning and reinforcement learning rely on, a bottleneck the source attributes to human effort rather than architecture.
Recent gains in language model capability have come more from data than from architecture. Frontier labs and data companies produce verifiable agentic tasks, which supervised finetuning and reinforcement learning then turn into capability.This production line still rests on human labour and on human
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