Author

Kaibing Yang

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#artificial intelligence Preprint Oct 2026

Credit Where It Matters: Dependency-Aware Policy Optimization for Terminal Agents

Terminal-using agents benefit from reinforcement learning (RL) in coding, debugging, and other multi-step terminal tasks. In these tasks, later commands often depend on information or intermediate results produced by earlier commands. However, existing trajectory-level and step-level credit assignment methods do not ex...

Yu Li, Guang-Feng Cai, Long-Fei Li et al. · 0 citations

Jul 2026

Progress-conditioned Group Policy Optimization for Long-Horizon Agentic Tasks

Progress-conditioned Group Policy Optimization is proposed, which uses first-visit observation coverage only when all samples in a group receive zero outcome reward, and consistently improves over group-based baselines, with particularly large gains on hard tasks.

Kaibing Yang, Guangfeng Cai, Sheng-Tian Yang et al. · arXiv.org · 1 citation