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
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
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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