Title: Problem-Parameter-Agnostic MAML
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Meta learning; MAML; Tuning-free

Abstract: Meta-learning aims to equip artificial intelligence systems with the ability to learn how to learn. Among its methods Model-Agnostic Meta-Learning (MAML) is particularly effective for enabling rapid task adaptation. However, vanilla MAML suffers from a critical drawback: its performance is highly sensitive to carefully tuned hyperparameters, especially learning rates. Since theoretically these learning rates depend on problem-specific factors (e.g., task heterogeneity and loss smoothness) that are typically unknown, this reliance hinders training stability and limits adaptation performance.
To address this challenge, we propose TFMAML, a tuning-free MAML algorithm that integrate adaptive stepsize and momentum techniques. 
TFMAML offers two key advantages: (i) it eliminates dependence on problem-specific parameters, allowing stepsizes to be pre-set without costly manual tuning or additional training process;
ii) it guarantees convergence, unlike vanilla MAML, which lacks convergence guarantees to first-order stationary points.
We provide rigorous theoretical analysis showing that TFMAML achieves the state-of-the-art convergence rate of $\mathcal{O}(\epsilon^{-4})$ to reach FOSP. Furthermore, we prove that the its first-order variant, TFFOMAML, avoids Hessian computations while retaining the same $\mathcal{O}(\epsilon^{-4})$ convergence rate. 
Unlike standard First-Order MAML, which suffers from a constant error floor, TFFOMAML eliminates this bias and converges reliably to stationary points.
Extensive experiments validate our theory, demonstrating the clear superiority of TFMAML and TFFOMAML over existing benchmarks.