DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yue, Murong, Yao, Wenlin, Mi, Haitao, Yu, Dian, Yao, Ziyu, Yu, Dong
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning
by: Yue, Murong, et al.
Published: (2023)

HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic Workflows
by: Yao, Wenlin, et al.
Published: (2024)

Group Distributionally Robust Optimization-Driven Reinforcement Learning for LLM Reasoning
by: Panaganti, Kishan, et al.
Published: (2026)

Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning
by: Liu, Rui, et al.
Published: (2025)

LiteSearch: Efficacious Tree Search for LLM
by: Wang, Ante, et al.
Published: (2024)

Stable and Efficient Single-Rollout RL for Multimodal Reasoning
by: Liu, Rui, et al.
Published: (2025)

Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
by: Zhang, Yuheng, et al.
Published: (2024)

Efficient but Vulnerable: Benchmarking and Defending LLM Batch Prompting Attack
by: Yue, Murong, et al.
Published: (2025)

Improving LLM General Preference Alignment via Optimistic Online Mirror Descent
by: Zhang, Yuheng, et al.
Published: (2025)

R-Zero: Self-Evolving Reasoning LLM from Zero Data
by: Huang, Chengsong, et al.
Published: (2025)

AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play
by: Xu, Ran, et al.
Published: (2025)

Unlocking Reasoning Capabilities in LLMs via Reinforcement Learning Exploration
by: Deng, Wenhao, et al.
Published: (2025)

From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
by: Wu, Xuansheng, et al.
Published: (2023)

Guided Self-Evolving LLMs with Minimal Human Supervision
by: Yu, Wenhao, et al.
Published: (2025)

Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
by: Tian, Ye, et al.
Published: (2024)

ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
by: Chen, Justin Chih-Yao, et al.
Published: (2023)

Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge
by: Tang, Yao, et al.
Published: (2026)

KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning
by: Xu, Hongling, et al.
Published: (2025)

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
by: Han, Yixuan, et al.
Published: (2025)

Reverse Thinking Makes LLMs Stronger Reasoners
by: Chen, Justin Chih-Yao, et al.
Published: (2024)

MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
by: Zhao, Guojiang, et al.
Published: (2025)

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
by: Dai, Runpeng, et al.
Published: (2025)

Mitigating Overthinking in Large Reasoning Models via Manifold Steering
by: Huang, Yao, et al.
Published: (2025)

R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning
by: Chen, Zhuokun, et al.
Published: (2025)

Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning
by: Yu, Erxin, et al.
Published: (2025)

Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
by: Deng, Yihe, et al.
Published: (2025)

Learning to Reason for Hallucination Span Detection
by: Su, Hsuan, et al.
Published: (2025)

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
by: DeepSeek-AI, et al.
Published: (2025)

Efficient Mathematical Reasoning Models via Dynamic Pruning and Knowledge Distillation
by: Yu, Fengming, et al.
Published: (2025)

Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
by: Liu, Mingjie, et al.
Published: (2025)

PCL-Reasoner-V1.5: Advancing Math Reasoning with Offline Reinforcement Learning
by: Lu, Yao, et al.
Published: (2026)

ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
by: Yang, Ling, et al.
Published: (2025)

Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
by: Guo, Zhenyuan, et al.
Published: (2026)

Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training
by: Liu, Yixin, et al.
Published: (2026)

Learning to Reason under Off-Policy Guidance
by: Yan, Jianhao, et al.
Published: (2025)

Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL
by: Yao, Jiarui, et al.
Published: (2025)

Compositional Generalization from Learned Skills via CoT Training: A Theoretical and Structural Analysis for Reasoning
by: Yao, Xinhao, et al.
Published: (2025)

Adaptive Test-Time Reasoning via Reward-Guided Dual-Phase Search
by: Cui, Yingqian, et al.
Published: (2025)

Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
by: Liu, Zijia, et al.
Published: (2025)

Incentivizing Agentic Reasoning in LLM Judges via Tool-Integrated Reinforcement Learning
by: Xu, Ran, et al.
Published: (2025)