AgentCPM-Explore: Realizing Long-Horizon Deep Exploration for Edge-Scale Agents
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917254425214976 |
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| author | Chen, Haotian Cong, Xin Fan, Shengda Fu, Yuyang Gong, Ziqin Lu, Yaxi Li, Yishan Niu, Boye Pan, Chengjun Song, Zijun Wang, Huadong Wu, Yesai Wu, Yueying Xie, Zihao Yan, Yukun Zhang, Zhong Lin, Yankai Liu, Zhiyuan Sun, Maosong |
| author_facet | Chen, Haotian Cong, Xin Fan, Shengda Fu, Yuyang Gong, Ziqin Lu, Yaxi Li, Yishan Niu, Boye Pan, Chengjun Song, Zijun Wang, Huadong Wu, Yesai Wu, Yueying Xie, Zihao Yan, Yukun Zhang, Zhong Lin, Yankai Liu, Zhiyuan Sun, Maosong |
| contents | While Large Language Model (LLM)-based agents have shown remarkable potential for solving complex tasks, existing systems remain heavily reliant on large-scale models, leaving the capabilities of edge-scale models largely underexplored. In this paper, we present the first systematic study on training agentic models at the 4B-parameter scale. We identify three primary bottlenecks hindering the performance of edge-scale models: catastrophic forgetting during Supervised Fine-Tuning (SFT), sensitivity to reward signal noise during Reinforcement Learning (RL), and reasoning degradation caused by redundant information in long-context scenarios. To address the issues, we propose AgentCPM-Explore, a compact 4B agent model with high knowledge density and strong exploration capability. We introduce a holistic training framework featuring parameter-space model fusion, reward signal denoising, and contextual information refinement. Through deep exploration, AgentCPM-Explore achieves state-of-the-art (SOTA) performance among 4B-class models, matches or surpasses 8B-class SOTA models on four benchmarks, and even outperforms larger-scale models such as Claude-4.5-Sonnet or DeepSeek-v3.2 in five benchmarks. Notably, AgentCPM-Explore achieves 97.09% accuracy on GAIA text-based tasks under pass@64. These results provide compelling evidence that the bottleneck for edge-scale models is not their inherent capability ceiling, but rather their inference stability. Based on our well-established training framework, AgentCPM-Explore effectively unlocks the significant, yet previously underestimated, potential of edge-scale models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06485 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AgentCPM-Explore: Realizing Long-Horizon Deep Exploration for Edge-Scale Agents Chen, Haotian Cong, Xin Fan, Shengda Fu, Yuyang Gong, Ziqin Lu, Yaxi Li, Yishan Niu, Boye Pan, Chengjun Song, Zijun Wang, Huadong Wu, Yesai Wu, Yueying Xie, Zihao Yan, Yukun Zhang, Zhong Lin, Yankai Liu, Zhiyuan Sun, Maosong Artificial Intelligence While Large Language Model (LLM)-based agents have shown remarkable potential for solving complex tasks, existing systems remain heavily reliant on large-scale models, leaving the capabilities of edge-scale models largely underexplored. In this paper, we present the first systematic study on training agentic models at the 4B-parameter scale. We identify three primary bottlenecks hindering the performance of edge-scale models: catastrophic forgetting during Supervised Fine-Tuning (SFT), sensitivity to reward signal noise during Reinforcement Learning (RL), and reasoning degradation caused by redundant information in long-context scenarios. To address the issues, we propose AgentCPM-Explore, a compact 4B agent model with high knowledge density and strong exploration capability. We introduce a holistic training framework featuring parameter-space model fusion, reward signal denoising, and contextual information refinement. Through deep exploration, AgentCPM-Explore achieves state-of-the-art (SOTA) performance among 4B-class models, matches or surpasses 8B-class SOTA models on four benchmarks, and even outperforms larger-scale models such as Claude-4.5-Sonnet or DeepSeek-v3.2 in five benchmarks. Notably, AgentCPM-Explore achieves 97.09% accuracy on GAIA text-based tasks under pass@64. These results provide compelling evidence that the bottleneck for edge-scale models is not their inherent capability ceiling, but rather their inference stability. Based on our well-established training framework, AgentCPM-Explore effectively unlocks the significant, yet previously underestimated, potential of edge-scale models. |
| title | AgentCPM-Explore: Realizing Long-Horizon Deep Exploration for Edge-Scale Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.06485 |