AutoDataBench: Can Agents Write the Data That Feeds the Self-Improvement Loop?
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-in-the-loop collaboration. Automating task creation would let data production scale with compute rather than with expert headcount, would extend to more domains, and would enable a key step in recursive self-improvement (RSI). Current evaluations of an agent's ability to write such tasks measure how a model performs after training on what the agent produced. That does not match common practice...
| Authors | Haotian Luo, Haoyu Wang, Zeyu Qin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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