Saved in:
| Main Authors: | Mahankali, Arvind, Wen, Kaiyue, Ma, Tengyu |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.23027 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Formal Theorem Proving by Rewarding LLMs to Decompose Proofs Hierarchically
by: Dong, Kefan, et al.
Published: (2024)
by: Dong, Kefan, et al.
Published: (2024)
Configuration-to-Performance Scaling Law with Neural Ansatz
by: Zhang, Huaqing, et al.
Published: (2026)
by: Zhang, Huaqing, et al.
Published: (2026)
Transitive RL: Value Learning via Divide and Conquer
by: Park, Seohong, et al.
Published: (2025)
by: Park, Seohong, et al.
Published: (2025)
Fantastic Pretraining Optimizers and Where to Find Them
by: Wen, Kaiyue, et al.
Published: (2025)
by: Wen, Kaiyue, et al.
Published: (2025)
Scaling Self-Play with Self-Guidance
by: Bailey, Luke, et al.
Published: (2026)
by: Bailey, Luke, et al.
Published: (2026)
Recursive Decomposition with Dependencies for Generic Divide-and-Conquer Reasoning
by: Hernández-Gutiérrez, Sergio, et al.
Published: (2025)
by: Hernández-Gutiérrez, Sergio, et al.
Published: (2025)
Pseudo-Formalization for Automatic Proof Verification
by: Barkallah, Slim, et al.
Published: (2026)
by: Barkallah, Slim, et al.
Published: (2026)
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)
Accurate and Scalable Matrix Mechanisms via Divide and Conquer
by: He, Guanlin, et al.
Published: (2026)
by: He, Guanlin, et al.
Published: (2026)
Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective
by: Wen, Kaiyue, et al.
Published: (2024)
by: Wen, Kaiyue, et al.
Published: (2024)
Diffusion Generative Modelling for Divide-and-Conquer MCMC
by: Trojan, C., et al.
Published: (2024)
by: Trojan, C., et al.
Published: (2024)
Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
by: Zhou, Yan, et al.
Published: (2026)
by: Zhou, Yan, et al.
Published: (2026)
Divide, Conquer, Combine Bayesian Decision Tree Sampling
by: Cochrane, Jodie A., et al.
Published: (2024)
by: Cochrane, Jodie A., et al.
Published: (2024)
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
by: Janati, Yazid, et al.
Published: (2024)
by: Janati, Yazid, et al.
Published: (2024)
Self-Verifying Reflection Helps Transformers with CoT Reasoning
by: Yu, Zhongwei, et al.
Published: (2025)
by: Yu, Zhongwei, et al.
Published: (2025)
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)
Dynamic Dual Buffer with Divide-and-Conquer Strategy for Online Continual Learning
by: Dai, Congren, et al.
Published: (2025)
by: Dai, Congren, et al.
Published: (2025)
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games
by: Zhang, Xiaoqing, et al.
Published: (2025)
by: Zhang, Xiaoqing, et al.
Published: (2025)
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression
by: Liu, Jiading, et al.
Published: (2022)
by: Liu, Jiading, et al.
Published: (2022)
Exploring the Limitations of Mamba in COPY and CoT Reasoning
by: Ren, Ruifeng, et al.
Published: (2024)
by: Ren, Ruifeng, et al.
Published: (2024)
CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning
by: Fang, Yuanheng, et al.
Published: (2025)
by: Fang, Yuanheng, et al.
Published: (2025)
PLM-eXplain: Divide and Conquer the Protein Embedding Space
by: van Eck, Jan, et al.
Published: (2025)
by: van Eck, Jan, et al.
Published: (2025)
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)
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
by: Röpke, Willem, et al.
Published: (2024)
by: Röpke, Willem, et al.
Published: (2024)
A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation
by: Kirchdorfer, Lukas, et al.
Published: (2025)
by: Kirchdorfer, Lukas, et al.
Published: (2025)
Divide-or-Conquer? Which Part Should You Distill Your LLM?
by: Wu, Zhuofeng, et al.
Published: (2024)
by: Wu, Zhuofeng, et al.
Published: (2024)
An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models
by: Zhang, Yizhou, et al.
Published: (2024)
by: Zhang, Yizhou, et al.
Published: (2024)
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning
by: Wang, Shang, et al.
Published: (2024)
by: Wang, Shang, et al.
Published: (2024)
What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
by: Feng, Yunzhen, et al.
Published: (2025)
by: Feng, Yunzhen, et al.
Published: (2025)
Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
by: Zhang, Weiliang, et al.
Published: (2025)
by: Zhang, Weiliang, et al.
Published: (2025)
Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoning
by: Yan, Shaotian, et al.
Published: (2026)
by: Yan, Shaotian, et al.
Published: (2026)
The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation
by: Lan, Yifan, et al.
Published: (2026)
by: Lan, Yifan, et al.
Published: (2026)
Divide, Harmonize, Then Conquer It: Shooting Multi-Commodity Flow Problems with Multimodal Language Models
by: Yuan, Xinyu, et al.
Published: (2026)
by: Yuan, Xinyu, et al.
Published: (2026)
A Short Note on Batch-efficient Divide-and-Conquer Algorithm for EigenDecomposition
by: Song, Yue
Published: (2026)
by: Song, Yue
Published: (2026)
Reducing Latency of LLM Search Agent via Speculation-based Algorithm-System Co-Design
by: Huang, Zixiao, et al.
Published: (2025)
by: Huang, Zixiao, et al.
Published: (2025)
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
by: Sennesh, Eli, et al.
Published: (2024)
by: Sennesh, Eli, et al.
Published: (2024)
Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning
by: Huang, Chengsong, et al.
Published: (2024)
by: Huang, Chengsong, et al.
Published: (2024)
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)
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)
How Likely Do LLMs with CoT Mimic Human Reasoning?
by: Bao, Guangsheng, et al.
Published: (2024)
by: Bao, Guangsheng, et al.
Published: (2024)
Similar Items
-
Formal Theorem Proving by Rewarding LLMs to Decompose Proofs Hierarchically
by: Dong, Kefan, et al.
Published: (2024) -
Configuration-to-Performance Scaling Law with Neural Ansatz
by: Zhang, Huaqing, et al.
Published: (2026) -
Transitive RL: Value Learning via Divide and Conquer
by: Park, Seohong, et al.
Published: (2025) -
Fantastic Pretraining Optimizers and Where to Find Them
by: Wen, Kaiyue, et al.
Published: (2025) -
Scaling Self-Play with Self-Guidance
by: Bailey, Luke, et al.
Published: (2026)