Loop Dropout: Regularizing Shared Updates in Looped Language Models
cs.LG, cs.CL
Submitted: 2026-09-28
Updated: 2026-10-04
Code: https://github.com/EleutherAI/lm-evaluation-harness
Terminology
Sources
- Program Synthesis with Large Language Models
- Deep Equilibrium Models
- Evaluating Large Language Models Trained on Code
- Training Verifiers to Solve Math Word Problems
- Universal Transformers
- QLoRA: Efficient Finetuning of Quantized LLMs
- Think-at-Hard: Dynamic Looped Transformers for Improved Reasoning
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
- Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
- Adaptive Computation Time for Recurrent Neural Networks
- LoRA+: Efficient Low Rank Adaptation of Large Models
- Measuring Massive Multitask Language Understanding
- Measuring Mathematical Problem Solving With the MATH Dataset
- LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
- Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
- LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation
- Step-resolved data attribution for looped transformers
- A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA
- Large Language Models are Zero-Shot Reasoners
- Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models
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