Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains through fine-tuning on domain-specific data. However, acquiring high-quality data for target domains remains a significant challenge. Existing data synthesis approaches foll...
Tong Ye, Hang Yu, Tengfei Ma et al.· Proceedings of the 32nd ACM...· 1 citation
Experiments with GRPO and GiGPO on ALFWorld, WebShop, and SearchQA show that both axes provide independent gains and that their combi- nation consistently achieves the strongest overall performance across model scales.
Ying-Xuan Zhuang, Bin-He Yu, Jing-Xiao Yang et al.· 1 citation
This work proposes TAMP-Nav, a unified framework for efficient embodied navigation that dynamically triggers Chain-of-Thought and retains high-fidelity memory only at critical nodes, compressing redundant trajectories into lightweight Space-Time Indicators, thereby preserving critical historical information and enhanci...
Hongyan Feng, Sun-Lai Chen, Xuan-Yu Liu et al.· 1 citation
This work investigates LLM-based forecasting agents, meaning systems in which a language model contributes to a scored prediction about a future or currently unobserved target, and organizes architectures into three groups.
Xiao-Gang Xu, Jiaqi Tang, Jianmin Chen et al.· 0 citations
This work proposes a novel framework, DOMINO, that learns a minimal sufficient domain representation from reference samples and leverages it to guide the generation of domain-aligned synthetic data, enabling practical and scalable domain adaptation without manual prompt design or natural language domain specifications.
Tong Ye, Hang Yu, Tengfei Ma et al.· Proceedings of the 32nd ACM...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
