Towards Agentic Self-Learning LLMs in Search Environment

• URL: http://arxiv.org/abs/2510.14253v2
• Date: Tue, 21 Oct 2025 02:16:23 GMT
• Title: Towards Agentic Self-Learning LLMs in Search Environment
• Authors: Wangtao Sun, Xiang Cheng, Jialin Fan, Yao Xu, Xing Yu, Shizhu He, Jun Zhao, Kang Liu,
• Abstract summary: We study whether self-learning can scale LLM-based agents without relying on human-curated datasets or predefined rule-based rewards.<n>We find that rewards from a Generative Reward Model (GRM) outperform rigid rule-based signals for open-domain learning.<n>We propose textbfAgentic Self-Learning (ASL), a fully closed-loop, multi-role reinforcement learning framework.
• Score: 36.158823302039195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study whether self-learning can scale LLM-based agents without relying on human-curated datasets or predefined rule-based rewards. Through controlled experiments in a search-agent setting, we identify two key determinants of scalable agent training: the source of reward signals and the scale of agent task data. We find that rewards from a Generative Reward Model (GRM) outperform rigid rule-based signals for open-domain learning, and that co-evolving the GRM with the policy further boosts performance. Increasing the volume of agent task data-even when synthetically generated-substantially enhances agentic capabilities. Building on these insights, we propose \textbf{Agentic Self-Learning} (ASL), a fully closed-loop, multi-role reinforcement learning framework that unifies task generation, policy execution, and evaluation within a shared tool environment and LLM backbone. ASL coordinates a Prompt Generator, a Policy Model, and a Generative Reward Model to form a virtuous cycle of harder task setting, sharper verification, and stronger solving. Empirically, ASL delivers steady, round-over-round gains, surpasses strong RLVR baselines (e.g., Search-R1) that plateau or degrade, and continues improving under zero-labeled-data conditions, indicating superior sample efficiency and robustness. We further show that GRM verification capacity is the main bottleneck: if frozen, it induces reward hacking and stalls progress; continual GRM training on the evolving data distribution mitigates this, and a small late-stage injection of real verification data raises the performance ceiling. This work establishes reward source and data scale as critical levers for open-domain agent learning and demonstrates the efficacy of multi-role co-evolution for scalable, self-improving agents. The data and code of this paper are released at https://github.com/forangel2014/Towards-Agentic-Self-Learning

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