$Cover Image$

An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Zui, Chen, Yezeng, Han, Jiaqi, Huang, Zhijie, Qi, Ji, Zhou, Yi
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Brain-Inspired Two-Stage Approach: Enhancing Mathematical Reasoning by Imitating Human Thought Processes
by: Chen, Yezeng, et al.
Published: (2024)

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
by: Huang, Kaixuan, et al.
Published: (2025)

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling
by: Liu, Zihan, et al.
Published: (2024)

ControlMath: Controllable Data Generation Promotes Math Generalist Models
by: Chen, Nuo, et al.
Published: (2024)

MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning
by: Li, Chengpeng, et al.
Published: (2023)

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

Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On
by: Zeng, Liang, et al.
Published: (2024)

AQA-Bench: An Interactive Benchmark for Evaluating LLMs' Sequential Reasoning Ability
by: Yang, Siwei, et al.
Published: (2024)

AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning
by: Chen, Yang, et al.
Published: (2025)

Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning
by: Li, Junsong, et al.
Published: (2025)

Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning
by: Singh, Joykirat, et al.
Published: (2024)

Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment after Instruction Tuning
by: Li, Guanlin, et al.
Published: (2025)

Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
by: Wang, Xinyi, et al.
Published: (2024)

AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy
by: Liu, Zihan, et al.
Published: (2025)

Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
by: Ji, Kaixuan, et al.
Published: (2024)

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
by: Wang, Peiyi, et al.
Published: (2023)

Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
by: Liu, Yijun, et al.
Published: (2024)

Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
by: Deng, Naihao, et al.
Published: (2024)

MathScale: Scaling Instruction Tuning for Mathematical Reasoning
by: Tang, Zhengyang, et al.
Published: (2024)

MegaMath: Pushing the Limits of Open Math Corpora
by: Zhou, Fan, et al.
Published: (2025)

A Case Study of Selected PTQ Baselines for Reasoning LLMs on Ascend NPU
by: Luo, Yuchen, et al.
Published: (2026)

BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs
by: Petrov, Ivo, et al.
Published: (2025)

FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline
by: Seegmiller, Parker, et al.
Published: (2025)

$π^2$: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models
by: Do, Quyet V., et al.
Published: (2026)

Can Post-Training Transform LLMs into Causal Reasoners?
by: Chen, Junqi, et al.
Published: (2026)

Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning
by: Yang, Wang, et al.
Published: (2025)

OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data
by: Toshniwal, Shubham, et al.
Published: (2024)

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

BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
by: Yang, Junxiao, et al.
Published: (2025)

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

KARL: Mitigating Hallucinations in LLMs via Knowledge-Boundary-Aware Reinforcement Learning
by: Gao, Cheng, et al.
Published: (2026)

On the Reasoning Abilities of Masked Diffusion Language Models
by: Svete, Anej, et al.
Published: (2025)

Towards Reasoning Ability of Small Language Models
by: Srivastava, Gaurav, et al.
Published: (2025)

AgentMath: Empowering Mathematical Reasoning for Large Language Models via Tool-Augmented Agent
by: Luo, Haipeng, et al.
Published: (2025)

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
by: Luo, Haipeng, et al.
Published: (2023)

Distilling LLMs' Decomposition Abilities into Compact Language Models
by: Tarasov, Denis, et al.
Published: (2024)

HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
by: Chen, Junying, et al.
Published: (2024)

Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners
by: Peng, Miao, et al.
Published: (2025)

RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
by: Liu, Wanlong, et al.
Published: (2024)

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
by: Shao, Zhihong, et al.
Published: (2024)