To CoT or To Loop? A Formal Comparison Between Chain-of-Thought and Looped Transformers
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Kevin, Sato, Issei |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Formal Comparison Between Chain of Thought and Latent Thought
by: Xu, Kevin, et al.
Published: (2025)
by: Xu, Kevin, et al.
Published: (2025)
CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring
by: Arnav, Benjamin, et al.
Published: (2025)
by: Arnav, Benjamin, et al.
Published: (2025)
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding
by: Xu, Kevin, et al.
Published: (2024)
by: Xu, Kevin, et al.
Published: (2024)
Reasoning with Latent Thoughts: On the Power of Looped Transformers
by: Saunshi, Nikunj, et al.
Published: (2025)
by: Saunshi, Nikunj, et al.
Published: (2025)
Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning
by: Yu, Qifan, et al.
Published: (2025)
by: Yu, Qifan, et al.
Published: (2025)
Cross Domain Evaluation of Multimodal Chain-of-Thought Reasoning of different datasets into the Amazon CoT Framework
by: Tiwari, Nitya, et al.
Published: (2025)
by: Tiwari, Nitya, et al.
Published: (2025)
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
by: Sprague, Zayne, et al.
Published: (2024)
by: Sprague, Zayne, et al.
Published: (2024)
CoT-ICL Lab: A Synthetic Framework for Studying Chain-of-Thought Learning from In-Context Demonstrations
by: Kothapalli, Vignesh, et al.
Published: (2025)
by: Kothapalli, Vignesh, et al.
Published: (2025)
Understanding Transformer Optimization via Gradient Heterogeneity
by: Tomihari, Akiyoshi, et al.
Published: (2025)
by: Tomihari, Akiyoshi, et al.
Published: (2025)
CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
by: Zhao, Qingqing, et al.
Published: (2025)
by: Zhao, Qingqing, et al.
Published: (2025)
The Ends Justify the Thoughts: RL-Induced Motivated Reasoning in LLM CoTs
by: Howe, Nikolaus, et al.
Published: (2025)
by: Howe, Nikolaus, et al.
Published: (2025)
Stability and Generalization in Looped Transformers
by: Labovich, Asher
Published: (2026)
by: Labovich, Asher
Published: (2026)
Fix Initial Codes and Iteratively Refine Textual Directions Toward Safe Multi-Turn Code Correction
by: Tanaka, Yuto, et al.
Published: (2026)
by: Tanaka, Yuto, et al.
Published: (2026)
Shorter Thoughts, Same Answers: Difficulty-Scaled Segment-Wise RL for CoT Compression
by: Tian, Ye, et al.
Published: (2026)
by: Tian, Ye, et al.
Published: (2026)
From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step
by: Deng, Yuntian, et al.
Published: (2024)
by: Deng, Yuntian, et al.
Published: (2024)
LoopQ: Quantization for Recursive Transformers
by: Fang, Rui, et al.
Published: (2026)
by: Fang, Rui, et al.
Published: (2026)
PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario
by: Mandalika, Sriram, et al.
Published: (2025)
by: Mandalika, Sriram, et al.
Published: (2025)
Relational Preference Encoding in Looped Transformer Internal States
by: Kirin, Jan
Published: (2026)
by: Kirin, Jan
Published: (2026)
Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models
by: Vendrell, Victor Conchello, et al.
Published: (2026)
by: Vendrell, Victor Conchello, et al.
Published: (2026)
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
by: Xie, Zeke, et al.
Published: (2020)
by: Xie, Zeke, et al.
Published: (2020)
Data Shifts Hurt CoT: A Theoretical Study
by: Yin, Lang, et al.
Published: (2025)
by: Yin, Lang, et al.
Published: (2025)
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
by: Park, Taekhyun, et al.
Published: (2026)
by: Park, Taekhyun, et al.
Published: (2026)
LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
by: Motwani, Sumeet Ramesh, et al.
Published: (2026)
by: Motwani, Sumeet Ramesh, et al.
Published: (2026)
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
by: Xu, Ruihan, et al.
Published: (2026)
by: Xu, Ruihan, et al.
Published: (2026)
The Kinetics of Reasoning: How Chain-of-Thought Shapes Learning in Transformers?
by: Pengmei, Zihan, et al.
Published: (2025)
by: Pengmei, Zihan, et al.
Published: (2025)
Simulation of Graph Algorithms with Looped Transformers
by: de Luca, Artur Back, et al.
Published: (2024)
by: de Luca, Artur Back, et al.
Published: (2024)
CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction
by: Park, Jueon, et al.
Published: (2025)
by: Park, Jueon, et al.
Published: (2025)
CITRAS: Covariate-Informed Transformer for Time Series Forecasting
by: Yamaguchi, Yosuke, et al.
Published: (2025)
by: Yamaguchi, Yosuke, et al.
Published: (2025)
Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought
by: Huang, Jianhao, et al.
Published: (2025)
by: Huang, Jianhao, et al.
Published: (2025)
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
by: Huang, Sili, et al.
Published: (2024)
by: Huang, Sili, et al.
Published: (2024)
What Makes Looped Transformers Perform Better Than Non-Recursive Ones
by: Gong, Zixuan, et al.
Published: (2025)
by: Gong, Zixuan, et al.
Published: (2025)
Inner Loop Inference for Pretrained Transformers: Unlocking Latent Capabilities Without Training
by: Lys, Jonathan, et al.
Published: (2026)
by: Lys, Jonathan, et al.
Published: (2026)
Compositional Reasoning with Transformers, RNNs, and Chain of Thought
by: Yehudai, Gilad, et al.
Published: (2025)
by: Yehudai, Gilad, et al.
Published: (2025)
Taking the GP Out of the Loop
by: Bafna, Mehul, et al.
Published: (2025)
by: Bafna, Mehul, et al.
Published: (2025)
The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation
by: Zhang, Ruichen, et al.
Published: (2025)
by: Zhang, Ruichen, et al.
Published: (2025)
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
by: Zhang, Zhen-Yu, et al.
Published: (2024)
by: Zhang, Zhen-Yu, et al.
Published: (2024)
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop
by: Picerno, Mario, et al.
Published: (2023)
by: Picerno, Mario, et al.
Published: (2023)
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
by: Gatmiry, Khashayar, et al.
Published: (2024)
by: Gatmiry, Khashayar, et al.
Published: (2024)
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning
by: Lou, Chenwei, et al.
Published: (2025)
by: Lou, Chenwei, et al.
Published: (2025)
Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer
by: Lu, Wenquan, et al.
Published: (2025)
by: Lu, Wenquan, et al.
Published: (2025)
Similar Items
-
A Formal Comparison Between Chain of Thought and Latent Thought
by: Xu, Kevin, et al.
Published: (2025) -
CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring
by: Arnav, Benjamin, et al.
Published: (2025) -
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding
by: Xu, Kevin, et al.
Published: (2024) -
Reasoning with Latent Thoughts: On the Power of Looped Transformers
by: Saunshi, Nikunj, et al.
Published: (2025) -
Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning
by: Yu, Qifan, et al.
Published: (2025)