Retrieval-Augmented Generation (RAG) im- proves the factuality of large language models (LLMs), yet existing RAG systems often struggle with complex, multi-step reasoning that requires adaptive retrieval and continuous revision of intermediate contexts. Recent reinforcement learning (RL)-based agentic RAG methods partially alleviate this issue, but typically rely on coarse-grained action spaces and trajectory-level rewards, resulting in weak reward assignment and a bias toward short-horizon, stereotyped reasoning template. To address, we propose 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. Experiments across a diverse set of multi-hop, open-domain, and agentic reasoning benchmarks, spanning multiple backbone model sizes, demonstrate that AGENTICRAG- R1 consistently outperforms strong baselines. Moreover, AGENTICRAG-R1 learns more robust, interpretable, and memory-aware reasoning behaviors, highlighting the effect of fine-grained action modeling and information- aware optimization for long-horizon reasoning. Our code is anonymous available at https: //github.com/jiangxinke/Harness-RL/ tree/AgenticRAG-R1-Whitebox.

As training regimes centered on human-curated data encounter rising costs and an increasingly binding scaling ceiling, a core question in agent learning shifts from enlarging exogenous datasets to enabling agents to **self-evolve** under limited exogenous supervision. Prior surveys on self-evolution and self-improvement typically organize the literature by evolutionary stages or system architectures. While effective for summarizing implementation pipelines, these perspectives do not offer a unified explanation of what an agent actually observes during self-improvement, nor why such signals can serve as sufficient and reliable supervision. To address this gap, we introduce **endogenous observations**, defined as learning signals that arise from an agent’s own generation, evaluation, and interaction processes rather than exogenous labels. We systematize existing approaches into three categories—model-internal, peer-induced, and scenario-grounded observations—according to how these signals are produced, and we summarize the characteristic properties and typical application settings of each category. Finally, we discuss key challenges in reliability, scalability, and long-horizon stability, and outline open directions toward robust and sustainable self-evolving agents.