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.
Build2SPARQL is a large-scale benchmark dataset for translating natural-language questions into SPARQL queries over building knowledge graphs. The dataset is generated by a KG‑grounded pipeline that produces 6,136 executable SPARQL queries and 30,680 corresponding natural-language questions across six query-pattern families and five vocabulary registers, covering 201 building KGs. Human validation shows high semantic fidelity, naturalness, and operational plausibility, and retrieval‑augmented evaluation demonstrates significant accuracy gains for open‑weight language models.
arXiv:2610.26932v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must int...
Goldilocks RL proposes an adaptive data‑selection strategy for reinforcement learning in language models, using a Selector network to predict reward variability and prioritize questions that are neither too easy nor too hard. By continuously adapting to the model’s evolving abilities, Goldilocks improves training efficiency on large‑scale reasoning datasets, achieving up to 78% fewer optimization steps compared to standard GRPO. The approach addresses the sample‑inefficiency problem of sparse rewards in reasoning tasks.
The paper introduces VagueBench, a benchmark that degrades seed queries into vague variants to evaluate large language models’ ability to ask follow‑up questions when user queries are incomplete. It proposes DialogGRPO, an offline reinforcement learning approach that models multi‑turn elicitation as a dialogue‑state directed acyclic graph, using dense turn‑level rewards to guide question selection. DialogGRPO achieves over 88% success in eliciting missing information on vague queries, improves answer coverage and recovery efficiency, and outperforms prompting and test‑time scaling baselines while generalizing zero‑shot to other dialogue datasets.
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...
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.
arXiv:2610.33511v1 Announce Type: new Abstract: Textual graph question answering (GraphQA) and graph-based retrieval-augmented generation (GraphRAG) have gained increasing attention as a way to groun...
The paper proposes a reinforcement learning framework that emphasizes designing feedback environments rather than solely relying on rewards. It integrates human domain knowledge via rubrics and model reasoning with hierarchical rewards to evaluate complex, partially successful outputs in open‑ended tasks. Applied to interactive web application generation, the model achieves 41.25% pass rate on MiniAppBench and 76.19% on ArtifactsBench, with ablation studies confirming the value of each component.
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.
arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.
The paper introduces a search‑aware reinforcement learning framework for multi‑component query understanding in Roblox game search. It first uses teacher‑student supervised fine‑tuning to create a schema‑compliant policy, then applies reinforcement learning that optimizes each query‑understanding component with component‑specific rewards derived from live search engine interactions. Experiments show that this approach improves per‑component utility and overall search quality, raising NDCG@20 by 8.9 points over the supervised baseline and 3.5 points over a single end‑to‑end reward strategy.