The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization

arXiv:2610.00983 · cs.CL, cs.AI · Submitted 2026-10-01 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/IntelChina-AI/RMSE

Terminology

• Quantization — 140× in this paper · explained in 12 episodes
• Loss Function — 18× in this paper · explained in 2 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Calibration — 7× in this paper · explained in 7 episodes
• Perplexity — 7× in this paper · explained in 3 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Quantization-Aware Training — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• Gemini: A Family of Highly Capable Multimodal Models
• Program Synthesis with Large Language Models
• Evaluating Large Language Models Trained on Code
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Training Verifiers to Solve Math Word Problems
• The Llama 3 Herd of Models
• DeepSeek-V3 Technical Report
• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
• Llama 2: Open Foundation and Fine-Tuned Chat Models

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