Layer-wise Curriculum Learning for Efficient LLM Compression

arXiv:2609.19213 · cs.LG, cs.AI · Submitted 2026-09-16 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: Accepted by the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2026

Code: https://github.com/mmai-laboratory/layer_wise_curriculum

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Pruning — 38× in this paper · explained in 4 episodes
• Distillation — 30× in this paper · explained in 2 episodes
• Knowledge Distillation — 14× in this paper · explained in 11 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Loss Function — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Ablation — 4× in this paper · explained in 1 episode
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Communication Overhead — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Perplexity — 3× in this paper · explained in 3 episodes
• Quantization — 3× in this paper · explained in 12 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Local Optimization — 2× in this paper · explained in 2 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distributed Data Parallel — 1× in this paper
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

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