Compiling Learning Problems into Adaptation Programs for Language Models

arXiv:2609.37371 · cs.CL, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Adaptation — 188× in this paper · explained in 1 episode
• Regret — 44× in this paper · explained in 3 episodes
• Calibration — 17× in this paper · explained in 7 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Fidelity — 5× in this paper · explained in 2 episodes
• Meta-learning — 5× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 5× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• The Llama 3 Herd of Models
• Sparse Structure Search for Parameter-Efficient Tuning
• Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
• Localized Adaptation Reveals Distinct Learning Signatures in Transformers
• Aletheia: Gradient-Guided Layer Selection for Efficient LoRA Fine-Tuning Across Architectures
• Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models
• AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
• Rethinking Adapter Placement: A Dominant Adaptation Module Perspective

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