Imagen de Portada

Dynamic Scaling of Unit Tests for Code Reward Modeling

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ma, Zeyao, Zhang, Xiaokang, Zhang, Jing, Yu, Jifan, Luo, Sijia, Tang, Jie
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language Software Engineering
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation
por: Ma, Zeyao, et al.
Publicado: (2024)

UnitCoder: Scalable Iterative Code Synthesis with Unit Test Guidance
por: Ma, Yichuan, et al.
Publicado: (2025)

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning
por: Zhang, Jiajun, et al.
Publicado: (2026)

Benchmarking LLMs for Unit Test Generation from Real-World Functions
por: Huang, Dong, et al.
Publicado: (2025)

Model Editing for LLMs4Code: How Far are We?
por: Li, Xiaopeng, et al.
Publicado: (2024)

Measuring the Influence of Incorrect Code on Test Generation
por: Huang, Dong, et al.
Publicado: (2024)

CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
por: Zhao, Yuwei, et al.
Publicado: (2024)

SRLCG: Self-Rectified Large-Scale Code Generation with Multidimensional Chain-of-Thought and Dynamic Backtracking
por: Ma, Hongru, et al.
Publicado: (2025)

DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Pattern
por: Yang, Lekang, et al.
Publicado: (2025)

TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation
por: Liu, Steven, et al.
Publicado: (2026)

Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis
por: Huang, Dong, et al.
Publicado: (2025)

Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination
por: Zheng, Jiasheng, et al.
Publicado: (2026)

MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms
por: Wang, Yibo, et al.
Publicado: (2025)

From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence
por: Yang, Jian, et al.
Publicado: (2025)

StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning
por: Wang, Hao, et al.
Publicado: (2026)

MaintainCoder: Maintainable Code Generation Under Dynamic Requirements
por: Wang, Zhengren, et al.
Publicado: (2025)

EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning
por: Huang, Dong, et al.
Publicado: (2024)

ReCode: Reinforcing Code Generation with Reasoning-Process Rewards
por: Fan, Lishui, et al.
Publicado: (2025)

ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models
por: Zheng, Jiasheng, et al.
Publicado: (2026)

CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation
por: Wang, Sizhe, et al.
Publicado: (2025)

CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding
por: Shi, Yuling, et al.
Publicado: (2026)

Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks
por: Zhao, Songwen, et al.
Publicado: (2025)

LongCodeZip: Compress Long Context for Code Language Models
por: Shi, Yuling, et al.
Publicado: (2025)

TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models
por: Park, Chansung, et al.
Publicado: (2026)

DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode
por: Han, Hojae, et al.
Publicado: (2026)

EffiBench: Benchmarking the Efficiency of Automatically Generated Code
por: Huang, Dong, et al.
Publicado: (2024)

CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale
por: Wang, Chenlong, et al.
Publicado: (2025)

HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation
por: Yu, Zhaojian, et al.
Publicado: (2024)

AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation
por: Luo, Ziyang, et al.
Publicado: (2024)

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
por: Chou, Jason, et al.
Publicado: (2025)

Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval
por: Wang, Jiexin, et al.
Publicado: (2024)

CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification
por: Tian, Yuchen, et al.
Publicado: (2024)

RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation
por: Li, Qingyao, et al.
Publicado: (2024)

Is Your Benchmark (Still) Useful? Dynamic Benchmarking for Code Language Models
por: Guan, Batu, et al.
Publicado: (2025)

Repoformer: Selective Retrieval for Repository-Level Code Completion
por: Wu, Di, et al.
Publicado: (2024)

Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education
por: Kumar, Nischal Ashok, et al.
Publicado: (2024)

GenX: Mastering Code and Test Generation with Execution Feedback
por: Wang, Nan, et al.
Publicado: (2024)

Scaling Test-Time Compute for Agentic Coding
por: Kim, Joongwon, et al.
Publicado: (2026)

DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale
por: Zhang, Linghao, et al.
Publicado: (2025)

Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?
por: Li, Xiangyang, et al.
Publicado: (2025)