A Layer-wise Analysis of Supervised Fine-Tuning

arXiv:2604.11838 · cs.LG, cs.AI · Submitted 2026-04-12 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-04-12

Updated: 2026-08-26

Comments: Accepted by ACL 2026 main conference

Code: https://github.com/lshowway/base

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

Terminology

• Alignment — 28× in this paper · explained in 1 episode
• Accuracy — 22× in this paper · explained in 1 episode
• Fine-Tuning — 17× in this paper · explained in 5 episodes
• Supervised Fine-Tuning — 11× in this paper
• Adaptation — 9× in this paper · explained in 1 episode
• Catastrophic Forgetting — 8× in this paper · explained in 5 episodes
• Sparsity — 8× in this paper · explained in 4 episodes
• Cosine Similarity — 7× in this paper · explained in 3 episodes
• Instruction Tuning — 7× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Perplexity — 4× in this paper · explained in 3 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation Studies — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment Techniques — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Activation Addition — 1× in this paper · explained in 1 episode
• Alignment Tax — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• BERT Rediscovers the Classical NLP Pipeline
• LIMA: Less Is More for Alignment

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