A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets. Their framework, LIMI (Less Is More for Intelligent Agency), finds that “machine autonomy emerges not from data abundance but from strategic curation of high-quality agentic demonstrations.” In experiments, the researchers found that with a small, but carefully curated, dataset of just 78 examples, they could train LLMs to outperform models trained on thousands of examples by a considerable margin on key industry benchmarks. This discovery could have important implications for enterprise applications where data is scarce or expensive to collect. The LIMI framework demonstrates that sophisticated agentic intelligence can emerge from minimal but strategically curated demonstrations of autonomous behavior. Key to the framework is a pipeline for collecting high-quality demonstrations of agentic tasks. The LIMI-trained model achieved an average score of 73.5% on AgencyBench, significantly outperforming all baseline models, the best of which (GLM-4.5) scored 45.1%. This superiority extended to other benchmarks covering tool use, coding, and scientific computing, where LIMI also outperformed all baselines. More importantly, the study showed that the model trained on just 78 examples outperformed models trained with 10,000 samples from another dataset, delivering superior performance with 128 times less data. “This discovery fundamentally reshapes how we develop autonomous AI systems, suggesting that mastering agency requires understanding its essence, not scaling training data,” the researchers write. Instead of undertaking massive data collection projects, organizations can leverage their in-house talent and subject matter experts to create small, high-quality datasets for bespoke agentic tasks. This lowers the barrier to entry and enables businesses to build custom AI agents that can provide a competitive edge on the workflows that matter most to them.