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About this paper
Jiarui Qin, Yunjia Xi, Junjie Huang, Renting Rui, Di Yin, Weiwen Liu, Yong Yu, Weinan Zhang and 1 more
A new benchmark called APTBench assesses the agentic potential of base Large Language Models during their pre-training phase by converting complex, multi-turn agent tasks into base-model-friendly formats. The benchmark provides a strong predictive signal for downstream agent performance and offers an economical evaluation method.
Key insights
- Agentic capabilities demonstrate an emergent property, with a clear performance gap between smaller and larger models, suggesting a critical parameter size threshold for their acquisition.
- The quality and agent-centric alignment of pre-training data are the most essential factors for developing effective agent base models, showing greater impact than just model size or architecture.
- Medium-sized models (around 30B parameters) can achieve competitive agentic performance comparable to or exceeding much larger Mixture-of-Experts models, suggesting a potential sweet spot for efficiency and capability.
Summary by alphaXiv.
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