LoopQ: Quantization for Looped Language Models
cs.LG, cs.AI
Submitted: 2026-05-08
Updated: 2026-10-12
Terminology
Sources
- Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Universal Transformers
- Looped Transformers for Length Generalization
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- The Pile: An 800GB Dataset of Diverse Text for Language Modeling
- A Persistent-State Dataflow Accelerator for Memory-Bound Linear Attention Decode on FPGA
- Measuring Massive Multitask Language Understanding
- Distilling the Knowledge in a Neural Network
- OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
- SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
- Transformer Fusion with Optimal Transport
- LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation
- TurboBoA: Faster and Exact Attention-aware Quantization without Backpropagation
- ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
- Pointer Sentinel Mixture Models
- Parcae: Scaling Laws For Stable Looped Language Models
- Pushing the Limits of Block Rotations in Post-Training Quantization
- Merging Feed-Forward Sublayers for Compressed Transformers
- Hyperloop Transformers
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