Buchumschlag

CodePMP: Scalable Preference Model Pretraining for Large Language Model Reasoning

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Yu, Huimu, Wu, Xing, Xu, Haotian, Zhang, Debing, Hu, Songlin
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Artificial Intelligence Computation and Language
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions
von: Gao, Chaochen, et al.
Veröffentlicht: (2025)

NExtLong: Toward Effective Long-Context Training without Long Documents
von: Gao, Chaochen, et al.
Veröffentlicht: (2025)

Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model
von: Gao, Chaochen, et al.
Veröffentlicht: (2024)

LongBench Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark
von: Chen, Ziyang, et al.
Veröffentlicht: (2026)

EntropyLong: Effective Long-Context Training via Predictive Uncertainty
von: Jia, Junlong, et al.
Veröffentlicht: (2025)

CCI4.0: A Bilingual Pretraining Dataset for Enhancing Reasoning in Large Language Models
von: Liu, Guang, et al.
Veröffentlicht: (2025)

Aligning Large Language Models with Searcher Preferences
von: Wu, Wei, et al.
Veröffentlicht: (2026)

Disentangling Reasoning and Knowledge in Medical Large Language Models
von: Thapa, Rahul, et al.
Veröffentlicht: (2025)

MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
von: Shi, Wenqi, et al.
Veröffentlicht: (2024)

Coupled Variational Reinforcement Learning for Language Model General Reasoning
von: Wen, Xueru, et al.
Veröffentlicht: (2025)

Libra: Large Chinese-based Safeguard for AI Content
von: Chen, Ziyang, et al.
Veröffentlicht: (2025)

To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks
von: Gong, Nanxu, et al.
Veröffentlicht: (2026)

DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation
von: Hu, Wenhao, et al.
Veröffentlicht: (2025)

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records
von: Shi, Wenqi, et al.
Veröffentlicht: (2024)

Large Language Models Could Be Rote Learners
von: Xu, Yuyang, et al.
Veröffentlicht: (2025)

Code-driven Number Sequence Calculation: Enhancing the inductive Reasoning Abilities of Large Language Models
von: Chen, Kedi, et al.
Veröffentlicht: (2025)

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
von: Xu, Fengli, et al.
Veröffentlicht: (2025)

A Survey on Large Language Models for Mathematical Reasoning
von: Wang, Peng-Yuan, et al.
Veröffentlicht: (2025)

TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
von: Chu, Zheng, et al.
Veröffentlicht: (2023)

Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social Media
von: Li, Kun, et al.
Veröffentlicht: (2024)

Code Pretraining Improves Entity Tracking Abilities of Language Models
von: Kim, Najoung, et al.
Veröffentlicht: (2024)

PediaMind-R1: A Temperament-Aware Language Model for Personalized Early Childhood Care Reasoning via Cognitive Modeling and Preference Alignment
von: Zhang, Zihe, et al.
Veröffentlicht: (2025)

Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies
von: Xiong, Tao, et al.
Veröffentlicht: (2025)

Parrot Mind: Towards Explaining the Complex Task Reasoning of Pretrained Large Language Models with Template-Content Structure
von: Yang, Haotong, et al.
Veröffentlicht: (2023)

Improving Attributed Text Generation of Large Language Models via Preference Learning
von: Li, Dongfang, et al.
Veröffentlicht: (2024)

Who Reasons in the Large Language Models?
von: Shao, Jie, et al.
Veröffentlicht: (2025)

Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up
von: Yuan, Jiahao, et al.
Veröffentlicht: (2024)

AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language Models
von: Zhang, Jinchuan, et al.
Veröffentlicht: (2025)

CodeMind: Evaluating Large Language Models for Code Reasoning
von: Liu, Changshu, et al.
Veröffentlicht: (2024)

Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge
von: Chen, Yongrui, et al.
Veröffentlicht: (2025)

LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
von: Hao, Shibo, et al.
Veröffentlicht: (2024)

MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models
von: Li, Jiazheng, et al.
Veröffentlicht: (2025)

Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data
von: Zhang, Xuemiao, et al.
Veröffentlicht: (2025)

Can Large Language Models do Analytical Reasoning?
von: Hu, Yebowen, et al.
Veröffentlicht: (2024)

MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining
von: Xiaomi, LLM-Core, et al.
Veröffentlicht: (2025)

Large Language Models as Code Executors: An Exploratory Study
von: Lyu, Chenyang, et al.
Veröffentlicht: (2024)

Weights-Rotated Preference Optimization for Large Language Models
von: Yang, Chenxu, et al.
Veröffentlicht: (2025)

Preference Packing: Efficient Preference Optimization for Large Language Models
von: Cho, Jaekyung
Veröffentlicht: (2026)

Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models
von: Tian, Junfeng, et al.
Veröffentlicht: (2024)

Reasoning Factual Knowledge in Structured Data with Large Language Models
von: Huang, Sirui, et al.
Veröffentlicht: (2024)