arXiv AI

Tool-Adaptive LLM Reranker

arXiv:2607. 10555v1 Announce Type: cross Abstract: Generative Large Language Models (LLMs) have revolutionized information retrieval, yet their strictly parametric nature frequently leads to severe factual hallucinations when confronted with complex queries beyond their epistemic boundaries.

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 AI

Aug 25

The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory

arXiv:2608.22767v1 Announce Type: new Abstract: Long-term language-model agents accumulate memories across interactions, but their retrievers typically do not accumulate retrieval experience. Semanti...

By Qi Feng, Chris Ding, Jicong Fan

llms rag agents

arXiv AI

Jun 9

Generative Reasoning Re-ranker

arXiv:2602. 07774v5 Announce Type: replace-cross Abstract: Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge.

By Mingfu Liang, Yufei Li, Jay Xu, Kavosh Asadi, Xi Liu, Shuo Gu, Kaushik Rangadurai, Frank Shyu, Shuaiwen Wang, Song Yang, Zhijing Li, Jiang Liu, Mengying Sun, Fei Tian, Xiaohan Wei, Chonglin Sun, Jacob Tao, Shike Mei, Wenlin Chen, Santanu Kolay, Sandeep Pandey, Hamed Firooz, Luke Simon

llms rag nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

4d ago

PrimeSeeker: Capability-Oriented Supervision for Deep Search Agents

arXiv:2609.35816v1 Announce Type: cross Abstract: Large language model search agents are often trained with synthetic questions whose difficulty is increased through larger evidence graphs, additiona...

By Linzhi Peng, Hanting Chen, Heng Chang, Ke Cheng, Bowen Du, Weifeng Lv

llms agents fine-tuning benchmarks

arXiv AI

4d ago

BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning

The paper introduces BRIDGE, a bilevel optimization framework that jointly trains a large language model (LLM) and a retriever for agentic reinforcement learning (ARL). It demonstrates that adapting the retriever before the policy yields better rewards, and that BRIDGE outperforms existing methods on seven open‑domain QA benchmarks and medical QA tasks, achieving significant gains in accuracy and reasoning quality.

By Quan Xiao, Mingda Liu, Gaowen Liu, Katsuki Fujisawa, Tianyi Chen

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jun 15

Optimizing Agentic Reasoning with Retrieval via Synthetic Semantic Information Gain Reward

arXiv:2602. 00845v3 Announce Type: replace Abstract: Agentic reasoning enables large reasoning models (LRMs) to dynamically acquire external knowledge, but yet optimizing the retrieval process remains challenging due to the lack of dense, principled reward signals.

By Senkang Hu, Yong Dai, Yuzhi Zhao, Yihang Tao, Yu Guo, Zhengru Fang, Sam Tak Wu Kwong, Yuguang Fang

rag agents benchmarks

arXiv Computation and Language

Sep 2

AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning

AdaSearch introduces a two‑stage reinforcement learning framework that separates problem solving from the decision to search in large language models. By using an F1‑based decision metric, it explicitly evaluates when external search is needed, reducing unnecessary search calls while maintaining high question‑answering performance. Experiments show that AdaSearch improves search‑decision quality with only a minor impact on accuracy compared to always‑search strategies.

By Tzu-Han Lin, Wei-Lin Chen, Chen-An Li, Hung-yi Lee, Yun-Nung Chen, Yu Meng

llms agents reinforcement-learning safety

arXiv Machine Learning

Jun 15

Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA

arXiv:2604. 23336v3 Announce Type: replace-cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial computational costs.

By Teng Chen, Sheng Xu, Feixiang Guo, Xiaoyu Wang, Qingqing Gu, Hongyan Li, Luo Ji

llms rag robotics efficiency benchmarks

arXiv AI

Aug 5

Training Documents Reranker with Search Rubrics for Deep Research Agent

arXiv:2608. 03527v1 Announce Type: cross Abstract: Retrieval systems help deep research agents generate high-quality answers by providing relevant documents.

By Wenhan Liu, Yu Lu, Qiaolin Xia, Hui Xu, Tong Zhao, Jian Xi, Yutao Zhu, Haijin Liang, Haibo Shi, Hao Wang, Zhicheng Dou

llms rag agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 16

RL-Index: Reinforcement Learning for Retrieval Index Reasoning

arXiv:2606. 16316v1 Announce Type: cross Abstract: Retrieving external knowledge is essential for solving real-world tasks, yet it remains challenging when the relationship between a query and its relevant knowledge involves implicit and complex reasoning beyond surface-level semantic or lexical matching (e.

By Yongjia Lei, Nedim Lipka, Zhisheng Qi, Utkarsh Sahu, Koustava Goswami, Franck Dernoncourt, Ryan A. Rossi, Yu Wang

llms agents reinforcement-learning efficiency benchmarks

arXiv AI

Aug 13

CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation

arXiv:2605. 04495v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily estimate query--document relevance.

By Zhipeng Song, Yizhi Zhou, Xiangyu Kong, Jiulong Jiao, Xuezhou Ye, Chunqi Gao, Xueqing Shi, Yu Wang, Yuhang Zhou, Heng Qi

llms rag

arXiv AI

Jun 6

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.

By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a

llms rag agents nlp fine-tuning benchmarks safety

Related stories