CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment
Fuente:
arXiv
Saved in:
| Main Authors: | Jiang, Xue, Dong, Yihong, Liu, Mengyang, Deng, Hongyi, Wang, Tian, Tao, Yongding, Cao, Rongyu, Li, Binhua, Jin, Zhi, Jiao, Wenpin, Huang, Fei, Li, Yongbin, Li, Ge |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Think Anywhere in Code Generation
by: Jiang, Xue, et al.
Published: (2026)
by: Jiang, Xue, et al.
Published: (2026)
Large Language Model Unlearning for Source Code
by: Jiang, Xue, et al.
Published: (2025)
by: Jiang, Xue, et al.
Published: (2025)
ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation
by: Jiang, Xue, et al.
Published: (2024)
by: Jiang, Xue, et al.
Published: (2024)
RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization
by: Dong, Yihong, et al.
Published: (2025)
by: Dong, Yihong, et al.
Published: (2025)
LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues
by: Lin, Yalan, et al.
Published: (2024)
by: Lin, Yalan, et al.
Published: (2024)
EvoCodeBench: An Evolving Code Generation Benchmark with Domain-Specific Evaluations
by: Li, Jia, et al.
Published: (2024)
by: Li, Jia, et al.
Published: (2024)
Exploring Data-Efficient Adaptation of Large Language Models for Code Generation
by: Jiang, Xue, et al.
Published: (2024)
by: Jiang, Xue, et al.
Published: (2024)
CodeScore: Evaluating Code Generation by Learning Code Execution
by: Dong, Yihong, et al.
Published: (2023)
by: Dong, Yihong, et al.
Published: (2023)
Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?
by: Pan, Zhenyu, et al.
Published: (2024)
by: Pan, Zhenyu, et al.
Published: (2024)
Do Code LLMs Understand Design Patterns?
by: Pan, Zhenyu, et al.
Published: (2025)
by: Pan, Zhenyu, et al.
Published: (2025)
Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration
by: Ma, Yingwei, et al.
Published: (2024)
by: Ma, Yingwei, et al.
Published: (2024)
Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute
by: Ma, Yingwei, et al.
Published: (2025)
by: Ma, Yingwei, et al.
Published: (2025)
From I/O to Code with Discovery Agent
by: Dong, Yihong, et al.
Published: (2026)
by: Dong, Yihong, et al.
Published: (2026)
Self-planning Code Generation with Large Language Models
by: Jiang, Xue, et al.
Published: (2023)
by: Jiang, Xue, et al.
Published: (2023)
Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model
by: Dong, Yihong, et al.
Published: (2025)
by: Dong, Yihong, et al.
Published: (2025)
Self-collaboration Code Generation via ChatGPT
by: Dong, Yihong, et al.
Published: (2023)
by: Dong, Yihong, et al.
Published: (2023)
EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories
by: Li, Jia, et al.
Published: (2024)
by: Li, Jia, et al.
Published: (2024)
Line-level Semantic Structure Learning for Code Vulnerability Detection
by: Wang, Ziliang, et al.
Published: (2024)
by: Wang, Ziliang, et al.
Published: (2024)
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
by: Li, Jia, et al.
Published: (2024)
by: Li, Jia, et al.
Published: (2024)
Rethinking Repetition Problems of LLMs in Code Generation
by: Dong, Yihong, et al.
Published: (2025)
by: Dong, Yihong, et al.
Published: (2025)
To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing
by: Cheng, Wei, et al.
Published: (2026)
by: Cheng, Wei, et al.
Published: (2026)
From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generation
by: Niu, Ke, et al.
Published: (2025)
by: Niu, Ke, et al.
Published: (2025)
Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models
by: Tao, Yongding, et al.
Published: (2025)
by: Tao, Yongding, et al.
Published: (2025)
Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format
by: Wang, Dingzirui, et al.
Published: (2025)
by: Wang, Dingzirui, et al.
Published: (2025)
A Survey on Code Generation with LLM-based Agents
by: Dong, Yihong, et al.
Published: (2025)
by: Dong, Yihong, et al.
Published: (2025)
Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement
by: Ma, Yingwei, et al.
Published: (2024)
by: Ma, Yingwei, et al.
Published: (2024)
IntentCoding: Amplifying User Intent in Code Generation
by: Fang, Zheng, et al.
Published: (2026)
by: Fang, Zheng, et al.
Published: (2026)
Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs
by: Zhu, Yuqi, et al.
Published: (2025)
by: Zhu, Yuqi, et al.
Published: (2025)
Empowering RepoQA-Agent based on Reinforcement Learning Driven by Monte-carlo Tree Search
by: Li, Guochang, et al.
Published: (2025)
by: Li, Guochang, et al.
Published: (2025)
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving
by: Mai, Xinji, et al.
Published: (2025)
by: Mai, Xinji, et al.
Published: (2025)
InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration
by: Wang, Yunkun, et al.
Published: (2025)
by: Wang, Yunkun, et al.
Published: (2025)
PseudoBridge: Pseudo Code as the Bridge for Better Semantic and Logic Alignment in Code Retrieval
by: Li, Yixuan, et al.
Published: (2025)
by: Li, Yixuan, et al.
Published: (2025)
ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
by: Wang, Yunkun, et al.
Published: (2024)
by: Wang, Yunkun, et al.
Published: (2024)
CodeDPO: Aligning Code Models with Self Generated and Verified Source Code
by: Zhang, Kechi, et al.
Published: (2024)
by: Zhang, Kechi, et al.
Published: (2024)
Reasoning is Periodicity? Improving Large Language Models Through Effective Periodicity Modeling
by: Dong, Yihong, et al.
Published: (2025)
by: Dong, Yihong, et al.
Published: (2025)
Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points
by: Zhang, Kechi, et al.
Published: (2025)
by: Zhang, Kechi, et al.
Published: (2025)
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning
by: Liu, Huanyu, et al.
Published: (2025)
by: Liu, Huanyu, et al.
Published: (2025)
Python Symbolic Execution with LLM-powered Code Generation
by: Wang, Wenhan, et al.
Published: (2024)
by: Wang, Wenhan, et al.
Published: (2024)
Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates
by: Sun, Zhihong, et al.
Published: (2024)
by: Sun, Zhihong, et al.
Published: (2024)
Executing as You Generate: Hiding Execution Latency in LLM Code Generation
by: Sun, Zhensu, et al.
Published: (2026)
by: Sun, Zhensu, et al.
Published: (2026)
Similar Items
-
Think Anywhere in Code Generation
by: Jiang, Xue, et al.
Published: (2026) -
Large Language Model Unlearning for Source Code
by: Jiang, Xue, et al.
Published: (2025) -
ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation
by: Jiang, Xue, et al.
Published: (2024) -
RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization
by: Dong, Yihong, et al.
Published: (2025) -
LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues
by: Lin, Yalan, et al.
Published: (2024)