arXiv Machine Learning

Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards

arXiv:2505. 04671v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) trained with reinforcement learning (RL) have improved Text-to-SQL performance.

arXiv AI

Jun 8

Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards

arXiv:2606. 06825v1 Announce Type: cross Abstract: Reinforcement learning has recently shown promise in improving large language models for Text-to-SQL generation, yet existing methods typically optimize one-shot rewards defined over a single SQL state.

By Shihao Zhang, Xiaoman Wang, Yuan Liu, Yunshi Lan, Weining Qian

llms reinforcement-learning safety data-engineering

arXiv AI

Jun 11

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.

By Jingpei Wu, Xiao Han, Weixiang Shen, Boer Zhang, Zifeng Ding, Volker Tresp

llms nlp reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 4

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

arXiv:2606. 04503v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset.

By Guangcheng Zhu, Shenzhi Yang, Haobo Wang, Xing Zheng, Yingfan MA, Xuening Feng, Zhongqi Chen, Bowen Song, Weiqiang Wang, Gang Chen

reinforcement-learning

arXiv AI

Jul 28

TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning

arXiv:2509. 06278v4 Announce Type: replace Abstract: Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations.

By Chuang Jiang, Mingyue Cheng, Xiaoyu Tao, Qingyang Mao, Jie Ouyang, Qi Liu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 1

Test-Time Verification for Text-to-SQL via Outcome Reward Models

arXiv:2606. 30851v1 Announce Type: cross Abstract: Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL.

By Mattia Tritto, Giuseppe Farano, Dario Di Palma, Gaetano Rossiello, Fedelucio Narducci, Dharmashankar Subramanian, Tommaso Di Noia

llms benchmarks safety data-engineering

arXiv AI

Jun 16

STRIDE: Strategic Trajectory Reasoning via Discriminative Estimation for Verifiable Reinforcement Learning

arXiv:2606. 15866v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training paradigm for improving the reasoning abilities of large language models.

By Qinjian Zhao, Zhihao Dou, Dinggen Zhang, Xiangyu Li, Chaoda Song, Zhongwei Wan, Xinpeng Li, Yanyan Zhang, Kaijie Chen, Qingtao Pan, Chengcheng Feng, Zhiqiang Gao, Xiaoyu Xia

llms agents reinforcement-learning

arXiv Machine Learning

Aug 12

Reinforcement Learning-based Semi-supervised Knowledge Distillation with LLM-as-a-Judge

arXiv:2604. 02621v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) substantially improves the reasoning capabilities of language models, but most existing RL fine-tuning approaches rely entirely on ground-truth verifiable rewards and thus labeled datasets with verifiable answers.

By Yiyang Shen, Lifu Tu, Weiran Wang

llms reinforcement-learning fine-tuning efficiency

arXiv AI

Aug 11

StructReward: Efficient Structured Process Rewards for Self-Correcting Multimodal Reasoning

arXiv:2608. 08326v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective approach for improving multimodal reasoning.

By Yifan Li, Ruxin Sun, Tongzhou Zhao

llms reinforcement-learning multimodal safety

arXiv Machine Learning

Jun 9

The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning

arXiv:2606. 09078v1 Announce Type: new Abstract: Process Reward Models (PRMs) improve credit assignment for reasoning by providing step-level feedback.

By Aakriti Agrawal, Souradip Chakraborty, Armin Saghafian, Nihal Sharma, Rizal Fathony, Nam H Nguyen, C. Bayan Bruss, Amrit Singh Bedi, Furong Huang

safety

arXiv AI

Jun 4

Aligning Deep Implicit Preferences by Learning to Reason Defensively

arXiv:2510. 11194v3 Announce Type: replace Abstract: Personalized alignment is crucial for enabling Large Language Models (LLMs) to engage effectively in user-centric interactions.

By Peiming Li, Zhiyuan Hu, Yang Tang, Shiyu Li, Xi Chen

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 29

The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment

arXiv:2606. 27739v1 Announce Type: new Abstract: Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs typically requires expensive stepwise annotations.

By Tianyu Jia, Yue Fang, Hongxin Ding, Rihong Qiu, Zhibang Yang, Zhijing Wu, Xu Chu, Junfeng Zhao, Yasha Wang

llms reinforcement-learning benchmarks

arXiv AI

Jun 2

Process Reward Agents for Steering Knowledge-Intensive Reasoning

arXiv:2604. 09482v2 Announce Type: replace Abstract: Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external knowledge sources.

By Jiwoong Sohn, Tomasz Sternal, Kenneth Styppa, Torsten Hoefler, Michael Moor

rag agents benchmarks

Related stories