arXiv Computation and Language

Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP

arXiv Machine Learning

Aug 6

RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learning

arXiv:2601. 05451v2 Announce Type: replace Abstract: Recent advances in text-to-SQL have been driven by larger models, better datasets, and new training methods like RLVR.

By Marko Sterbentz, Kevin Cushing, Cameron Barrie, Kristian J. Hammond

llms reinforcement-learning benchmarks data-engineering

arXiv Computation and Language

Aug 25

GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning

arXiv:2608.22479v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop quest...

By Jun Chen, Yongchao Liu, Pengyu Qiu, Jiajun Zheng, Juelu Zhang, Yujie Zeng, Qin Zhang, Ziyue Qiao, Xiao Luo

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jul 21

DeLIVeR: Decomposed Learning for Information-grounded Veracity Recognition via Reinforced Knowledge Graph Exploration

arXiv:2607. 17935v1 Announce Type: cross Abstract: Automated fact-checking remains a challenge for Large Language Models (LLMs) due to "query brittleness" in traditional retrieval systems.

By Cong Hoan Nguyen, Thomas Hoang, Hieu Minh Duong, Long Nguyen

llms benchmarks

arXiv AI

Jul 22

Dr. Zero: Self-Evolving Search Agents without Training Data

arXiv:2601. 07055v2 Announce Type: replace Abstract: As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm.

By Zhenrui Yue, Kartikeya Upasani, Xianjun Yang, Suyu Ge, Shaoliang Nie, Yuning Mao, Zhe Liu, Dong Wang

llms agents nlp benchmarks

arXiv AI

Jul 3

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.

By Yuhang Wu, Xiangqing Shen, Fanfan Wang, Cangqi Zhou, Zhen Wu, Xinyu Dai, Rui Xia

llms rag reinforcement-learning benchmarks

arXiv Machine Learning

Jun 10

$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data

arXiv:2605. 01248v3 Announce Type: replace Abstract: Reinforcement learning (RL) post-training has enabled newer capabilities in models, such as agentic tool-use for search.

By Harsh Goel, Akhil Udathu, Susmija Jabbireddy, Pradnesh Kalkar, Atharva Parulekar

agents reinforcement-learning

arXiv AI

Aug 24

SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation

arXiv:2509.20377v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has significantly improved the performance of large language models (LLMs) on knowledge-intensive tasks...

By Tomoaki Isoda

llms rag nlp reinforcement-learning benchmarks safety

arXiv AI

Jun 3

QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards

arXiv:2606. 03968v1 Announce Type: cross Abstract: Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as fixed.

By Rongzhi Zhang, Rui Feng, Zhihan Zhang, Jingfeng Yang, Qingyu Yin, Xin Liu, Zixuan Zhang, Priyanka Nigam, Bing Yin, Tuo Zhao, Chao Zhang

reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Sep 1

Small Language Models as Judges for Rubric-Based Reinforcement Learning

arXiv:2608.30005v1 Announce Type: new Abstract: Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scoring responses against instance-specific c...

By Fengyu Xie, Yilun Zhao, Bingsen Chen, Arman Cohan, Chen Zhao

llms reinforcement-learning

arXiv Computation and Language

2d ago

COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training

arXiv:2609.20842v1 Announce Type: new Abstract: Text-to-SQL translates natural-language questions into executable SQL queries, but open-source large language models still require task-specific post-t...

By Qifeng Cai, Xuanguang Pan, Hao Liang, Chang Xu, Wentao Zhang

llms reinforcement-learning fine-tuning data-engineering

arXiv AI

Jul 14

GRASP: GRanularity-Aware Search Policy for Agentic RAG

arXiv:2607. 10463v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.

By Varun Gandhi, Jaewook Lee, Shantanu Todmal, Franck Dernoncourt, Ryan Rossi, Zichao Wang, Andrew Lan

llms rag agents nlp reinforcement-learning benchmarks

arXiv AI

Aug 11

Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewards

arXiv:2608. 07531v1 Announce Type: cross Abstract: Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence.

By Cheng Ruoxi, Ma Haoxuan, Zhang Hongyi, Zhang Junming, Duan Ranjie, Xia Qiaolin, Wang Hao, Lu Yu, Shi Haibo, Ma Xingjun

llms agents reinforcement-learning benchmarks

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