Skills, as reusable external knowledge, have demonstrated substantial potential to enhance the ability of Large Language Models (LLMs) to solve complex tasks. However, existing skill-augmented generation methods typically rely on separate models for skill retrieval and skill utilization, resulting in disconnected optimization of these two processes. To address this gap, we propose Skill-Uni, a unified reinforcement learning framework that jointly optimizes skill retrieval and utilization. Specifically, we adopt a generative skill retrieval paradigm that enables a single model to both learn how to retrieve relevant skills and leverage them for task solving. To effectively train this unified framework, we introduce a two-stage training strategy consisting of warm-up instruction tuning and GRPO, allowing the model to progressively improve its skill retrieval and utilization capabilities. Extensive experiments on five skill-related benchmarks demonstrate the effectiveness of Skill-Uni, yielding average performance gains of 66.59% and 34.82% over the base Qwen3-4B and LLaMA3.1-8B models, respectively. Besides, Skill-Uni is compatible with various skill retrieval engines, demonstrating its flexibility and generalizability. Additional in-depth analyses also provide insights into the effectiveness of Skill-Uni.
Han Luo, Guy Laban Ben Gurion University of the Negev
LLM-based agents are increasingly deployed for mental-health support and crisis counselling, yet recent evaluations reveal that commercial therapy chatbots respond appropriately only about half the time in clinical scenarios.Clinicians and safety engineers are called upon to audit these systems, but existing tools do not surface the subtler psychosocial harms (manipulation, discrimination, psychological distress) nor produce the explainable rationales that practitioners need.We present DialogGuard, an open-source system that lets practitioners inspect, stress-test, and create audit trails for prompted LLM agents across five psychosocial safety dimensions.The system wraps around arbitrary generative models through four LLM-as-a-judge pipelines (single-agent scoring, dual-agent correction, multi-agent debate, and majority voting), each grounded in shared three-level rubrics.Through its web interface, practitioners evaluate agents in two modes (Live Chat and Manual Input) and review per-dimension risk scores with natural-language rationales.Experiments on PKU-SafeRLHF show that dual-agent correction provides the best accuracy-robustness trade-off, and a formative study with 12 practitioners confirms that the system supports prompt auditing, safety inspection, and supervisory decision-making.Code and demo: https://github.com/lhannnn/dialogguard-web.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表