Copertina

AgentV-RL: Scaling Reward Modeling with Agentic Verifier

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Jiazheng, Fu, Ziche, Xi, Zhiheng, Jing, Wenqing, Chai, Mingxu, He, Wei, Zhang, Guoqiang, Fan, Chenghao, An, Chenxin, Chen, Wenxiang, Liu, Zhicheng, Pan, Haojie, Zhu, Dingwei, Gui, Tao, Zhang, Qi, Huang, Xuanjing
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Computation and Language Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

VRPO: Rethinking Value Modeling for Robust RL Training under Noisy Supervision
di: Zhu, Dingwei, et al.
Pubblicazione: (2025)

Two Minds Better Than One: Collaborative Reward Modeling for LLM Alignment
di: Zhang, Jiazheng, et al.
Pubblicazione: (2025)

Can RL Improve Generalization of LLM Agents? An Empirical Study
di: Xi, Zhiheng, et al.
Pubblicazione: (2026)

Better Process Supervision with Bi-directional Rewarding Signals
di: Chen, Wenxiang, et al.
Pubblicazione: (2025)

Prefix-Adaptive Block Diffusion for Efficient Document Recognition
di: Chai, Mingxu, et al.
Pubblicazione: (2026)

Entropy Polarity in Reinforcement Fine-Tuning: Direction, Asymmetry, and Control
di: Zhang, Jiazheng, et al.
Pubblicazione: (2026)

DFPO: Scaling Value Modeling via Distributional Flow towards Robust and Generalizable LLM Post-Training
di: Zhu, Dingwei, et al.
Pubblicazione: (2026)

OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment
di: Zhang, Ming, et al.
Pubblicazione: (2026)

CCTU: A Benchmark for Tool Use under Complex Constraints
di: Ye, Junjie, et al.
Pubblicazione: (2026)

Verifiable Process Rewards for Agentic Reasoning
di: Yuan, Huining, et al.
Pubblicazione: (2026)

Scaling Agentic Verifier for Competitive Coding
di: Ma, Zeyao, et al.
Pubblicazione: (2026)

Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals
di: Zheng, Rui, et al.
Pubblicazione: (2024)

AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress
di: Xi, Zhiheng, et al.
Pubblicazione: (2025)

RoCoIns: Enhancing Robustness of Large Language Models through Code-Style Instructions
di: Zhang, Yuansen, et al.
Pubblicazione: (2024)

Critique to Verify: Accurate and Honest Test-Time Scaling with RL-Trained Verifiers
di: Yang, Zhicheng, et al.
Pubblicazione: (2025)

Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains
di: Su, Yi, et al.
Pubblicazione: (2025)

Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
di: Tong, Jingqi, et al.
Pubblicazione: (2025)

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models
di: Zhou, Xin, et al.
Pubblicazione: (2025)

Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation
di: Zhang, Chenghao, et al.
Pubblicazione: (2026)

From Scores to Preferences: Redefining MOS Benchmarking for Speech Quality Reward Modeling
di: Cao, Yifei, et al.
Pubblicazione: (2025)

JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees
di: Wang, Yuhui, et al.
Pubblicazione: (2026)

AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
di: Xi, Zhiheng, et al.
Pubblicazione: (2025)

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
di: Lin, Jiahang, et al.
Pubblicazione: (2026)

LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening
di: Zhang, Ming, et al.
Pubblicazione: (2026)

Chart-RL: Generalized Chart Comprehension via Reinforcement Learning with Verifiable Rewards
di: Zhang, Xin, et al.
Pubblicazione: (2026)

Polar: Agentic RL on Any Harness at Scale
di: Xu, Binfeng, et al.
Pubblicazione: (2026)

DocFusion: A Unified Framework for Document Parsing Tasks
di: Chai, Mingxu, et al.
Pubblicazione: (2024)

The Implicit Curriculum: Learning Dynamics in RL with Verifiable Rewards
di: Huang, Yu, et al.
Pubblicazione: (2026)

EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training
di: Pan, Chengjun, et al.
Pubblicazione: (2026)

When Sharpening Becomes Collapse: Sampling Bias and Semantic Coupling in RL with Verifiable Rewards
di: Fan, Mingyuan, et al.
Pubblicazione: (2026)

Let it Calm: Exploratory Annealed Decoding for Verifiable Reinforcement Learning
di: Yang, Chenghao, et al.
Pubblicazione: (2025)

LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
di: Zhang, Ming, et al.
Pubblicazione: (2025)

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
di: Zhang, Ming, et al.
Pubblicazione: (2025)

RMB: Comprehensively Benchmarking Reward Models in LLM Alignment
di: Zhou, Enyu, et al.
Pubblicazione: (2024)

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement
di: Xi, Zhiheng, et al.
Pubblicazione: (2023)

Improving RL Exploration for LLM Reasoning through Retrospective Replay
di: Dou, Shihan, et al.
Pubblicazione: (2025)

Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards
di: Li, Ming, et al.
Pubblicazione: (2025)

ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning
di: Tang, Lingxiao, et al.
Pubblicazione: (2026)

LLM4PM: A case study on using Large Language Models for Process Modeling in Enterprise Organizations
di: Ziche, Clara, et al.
Pubblicazione: (2024)

IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
di: Li, Yuhan, et al.
Pubblicazione: (2026)