Image de couverture de livre

ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Cheng, Mingfei, Xie, Xiaofei, Wang, Renzhi, Zhou, Yuan, Hu, Ming
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence Software Engineering
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

STCLocker: Deadlock Avoidance Testing for Autonomous Driving Systems
par: Cheng, Mingfei, et autres
Publié: (2025)

DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing
par: Cheng, Mingfei, et autres
Publié: (2024)

MoDitector: Module-Directed Testing for Autonomous Driving Systems
par: Wang, Renzhi, et autres
Publié: (2025)

Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems
par: Cheng, Mingfei, et autres
Publié: (2024)

Causality-aware Safety Testing for Autonomous Driving Systems
par: Tang, Wenbing, et autres
Publié: (2025)

MORTAR: A Model-based Runtime Action Repair Framework for AI-enabled Cyber-Physical Systems
par: Wang, Renzhi, et autres
Publié: (2024)

Moral Testing of Autonomous Driving Systems
par: Tang, Wenbing, et autres
Publié: (2025)

Drivora: A Unified and Extensible Infrastructure for Search-based Autonomous Driving Testing
par: Cheng, Mingfei, et autres
Publié: (2026)

ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs
par: Kong, Jiaolong, et autres
Publié: (2024)

Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing
par: Wang, Wenhan, et autres
Publié: (2025)

Foundation Models for Autonomous Driving System: An Initial Roadmap
par: Wu, Xiongfei, et autres
Publié: (2025)

FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion
par: Guo, Qi, et autres
Publié: (2024)

On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software
par: Nouri, Ali, et autres
Publié: (2025)

Repair-R1: Better Test Before Repair
par: Hu, Haichuan, et autres
Publié: (2025)

PentestEval: Benchmarking LLM-based Penetration Testing with Modular and Stage-Level Design
par: Yang, Ruozhao, et autres
Publié: (2025)

AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications
par: Yang, Ruozhao, et autres
Publié: (2026)

RepairAgent: An Autonomous, LLM-Based Agent for Program Repair
par: Bouzenia, Islem, et autres
Publié: (2024)

LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos
par: Tian, Haoxiang, et autres
Publié: (2024)

SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning
par: Ma, Zexiong, et autres
Publié: (2025)

GiFT: Gibbs Fine-Tuning for Code Generation
par: Li, Haochen, et autres
Publié: (2025)

Detecting Non-Optimal Decisions of Embodied Agents via Diversity-Guided Metamorphic Testing
par: Wu, Wenzhao, et autres
Publié: (2025)

Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
par: Zhang, Lingzhe, et autres
Publié: (2026)

E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning
par: Zhang, Lingzhe, et autres
Publié: (2026)

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
par: Yanuganti, Vasudha, et autres
Publié: (2025)

Large Language Models in Code Co-generation for Safe Autonomous Vehicles
par: Nouri, Ali, et autres
Publié: (2025)

Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
par: Chen, Jie, et autres
Publié: (2024)

Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models
par: Wang, Jian, et autres
Publié: (2025)

FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair
par: Ma, Ruize, et autres
Publié: (2026)

FixDrive: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per Violation
par: Sun, Yang, et autres
Publié: (2025)

Beyond Accuracy: Policy Invariance as a Reliability Test for LLM Safety Judges
par: Weng, Shihao, et autres
Publié: (2026)

Enhancing Automated Program Repair via Faulty Token Localization and Quality-Aware Patch Refinement
par: Kong, Jiaolong, et autres
Publié: (2025)

Generative AI for Testing of Autonomous Driving Systems: A Survey
par: Song, Qunying, et autres
Publié: (2025)

BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models
par: Li, Yuanhao, et autres
Publié: (2026)

PAFT: Preservation Aware Fine-Tuning for Minimal-Edit Program Repair
par: Yang, Boyang, et autres
Publié: (2026)

Rethinking the Capability of Fine-Tuned Language Models for Automated Vulnerability Repair
par: Han, Woorim, et autres
Publié: (2025)

LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward
par: Islam, Nafis Tanveer, et autres
Publié: (2024)

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs
par: Sakharova, Marina, et autres
Publié: (2025)

Simulator Ensembles for Trustworthy Autonomous Driving Testing
par: Sorokin, Lev, et autres
Publié: (2025)

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents
par: Huang, Jiawei, et autres
Publié: (2026)

AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis
par: Yu, Jiongchi, et autres
Publié: (2025)