GPT-5.6 Helps Prove New Lower Bound for Gradient Descent

Researchers Jianhao Ma and Yuxin Chen used GPT-5.6 Sol Pro to establish a new lower bound of roughly Ω(T^-1.931) for gradient descent convergence in smooth convex optimization, showing that tuning step sizes alone cannot reach the optimal O(T^-2) rate. The model only provided high-level strategic guidance.

2026-08-17 ~ 2026-08-17 · 2 related posts

• Math proof allegedly developed by GPT-5.6 sets new optimization convergence bounds — Dr_Singularity · 2026-08-17
• GPT-5.6 used to prove new gradient descent lower bound — PMinervini · 2026-08-17