Uncertainty Makes It Stable: Curiosity-Driven Quantized Mixture-of-Experts

arXiv:2511.11743 · cs.LG, cs.AI · Submitted 2025-11-13 · Read on arXiv

cs.LG, cs.AI

Submitted: 2025-11-13

Updated: 2026-08-29

Code: https://github.com/sebasmos/curious-qmoe

Terminology

• Quantization — 70× in this paper · explained in 12 episodes
• Accuracy — 30× in this paper · explained in 1 episode
• Epistemic Uncertainty — 15× in this paper · explained in 9 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Post-Training Quantization — 6× in this paper
• Ablation — 5× in this paper · explained in 1 episode
• Edge Deployment — 5× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Mixture of Experts — 5× in this paper · explained in 2 episodes
• Monte Carlo dropout — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Edge Devices — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Quantization-Aware Training — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Energy Efficiency — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Mixture of Experts (MoE) — 1× in this paper · explained in 5 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

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