Author

Yang Yang

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Jun 2026

Stage-Transition Dense Reward Modeling for Reinforcement Learning

Experiments show that STDR consistently improves sample efficiency and success rates over multiple baselines, and matches or surpasses handcrafted dense rewards on several challenging tasks, suggesting robustness to visual noise and better-calibrated reward assignment across settings.

Yang Yang, Bingjie Chen, Zihan Wang et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Aug 2026

DART-SD: Diamond-topology Aware Retrieval and Tuning for Self-Distillation of Multi-Turn Tool-Calling Agents

This work proposes DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation), a novel framework that shifts the paradigm from global forcing to topology-guided localized correction, and significantly outperforms traditional full-trajectory baselines.

Hangrui Xu, Jiarui Wang, Yang Yang et al. · 0 citations