Large Language Models (LLMs) are prone to generating hallucinated content, which compromises their reliability in knowledge-intensive tasks. To address this challenge without sacrificing creativity, we propose HARPO, a reinforcement learning framework designed to jointly optimize faithfulness and creativity. HARPO inco...
Tie-Zheng Yu, Yu-Xin Jiang, Jin-Peng Li et al.· 0 citations
On-policy Verbal Distillation is introduced, a framework that uses verbal scores from black-box teachers to rank student-generated sub-trajectories, retaining high-scoring ones and replacing low-scoring ones with teacher-generated continuations and suggests that retaining student-generated prefixes helps preserve explo...
Jing Xiong, Hui Shen, Shansan Gong et al.· arXiv.org· 8 citations
EfficientAgent is presented, which sizes and manages the host tier by this working set, which estimates the working set from agent histories to size the host tier and its predictions, made before the experiments, located the capacity at which offloading starts to pay.
Kun-Ming Shao, Jie-Run Chen, Jiang-Nan Yu et al.· 0 citations
Experiments show that agentic review continuously improves PRs through a generate-review-revise loop, outperforms single-turn fixed-context review in both decision accuracy and resolve rate after revision, transfers beyond review to improve issue-resolution models, and enables effective and efficient test-time scaling.
Ruoyu Wang, Jierun Chen, Shaowei Wang et al.· arXiv.org· 4 citations
This work presents OptiMAS, a task-agnostic agentic optimizer that leverages textual interaction trajectories and task feedback as loss signals for end-to-end MAS evolution and sustains performance improvement over extended optimization horizons.
Yuxin Cheng, Chang Liu, Hanxin Yu et al.· 0 citations
Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execution feedback. However, the native execution environments of these harnesses are inherently misaligned with policy-gradient training: environmental crashes and reward ha...
Yi-Ming Du, Yu-Xin Jiang, Tao Yuan et al.· 3 citations
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