Author

Rihong Qiu

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Preprint Aug 2026

Harness-RL: Black-Box Reinforcement Learning with Action-Args Decoupling for Central-Agent Multi-Agent Harnesses

Harness-RL is introduced, a structured reinforcement learning framework that combines Conflict-Aware Policy Optimization (CAPO) with interface-level black-box trajectory construction and supports both central-only and joint multi-agent training.

Xin-Ke Jiang, Zhi-Xin Zhang, Zhi-Bang Yang et al. · 1 citation

#artificial intelligence Preprint Aug 2026

AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing

This work proposes AgenticRag-R1, a RL framework that deeply integrates reasoning, retrieval, and memory via a memory stack and fine-grained action space, supported by hierarchical action-aware rewards and an information-aware trajectory rejection strategy to enable effective long-horizon learning.

Xin-Ke Jiang, Yue Fang, Zhi-Bang Yang et al. · 2 citations