A 35-billion-parameter model called Agents-A1 matches trillion-parameter models on multi-step agent tasks, according to a new paper from Shanghai AI Lab. The key insight: instead of scaling parameter count, the researchers scaled the "horizon" — the length and variety of action sequences the model trains on — producing a small model that sustains plans across long sequences of tool use as well as giants do. The work is on arXiv, and its title captures the thesis: scaling the horizon, not the parameters.
Key facts
What: Shanghai AI Lab argues you can reach giant-model performance on long tasks not by adding parameters, but by training on much longer chains of real work.
When: 2026-06-30
Primary source: read the source (arXiv 2606.30616)
