What Does Model Growth Really Add? Functional Capacity Beyond Parameter Count

arXiv:2607.14571 · cs.LG, cs.AI · Submitted 2026-07-16 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-07-16

Updated: 2026-09-27

Terminology

• Ablation — 26× in this paper · explained in 1 episode
• Continual Learning — 15× in this paper · explained in 2 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Distillation — 13× in this paper · explained in 2 episodes
• Perplexity — 7× in this paper · explained in 3 episodes
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Redundancy — 5× in this paper · explained in 2 episodes
• Additivity — 4× in this paper · explained in 1 episode
• Catastrophic Forgetting — 4× in this paper · explained in 5 episodes
• Fisher Information — 4× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 4× in this paper
• Gradient Flow — 3× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Degeneracy — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• alpha — 2× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• International Conference on Machine Learning — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Progressive Neural Networks
• GLU Variants Improve Transformer

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks