WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866912805827903488 |
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| author | Yu, Xinmiao Zhang, Liwen Feng, Xiaocheng Jiang, Yong Qin, Bing Xie, Pengjun Zhou, Jingren |
| author_facet | Yu, Xinmiao Zhang, Liwen Feng, Xiaocheng Jiang, Yong Qin, Bing Xie, Pengjun Zhou, Jingren |
| contents | Large Language Model(LLM)-based agents have shown strong capabilities in web information seeking, with reinforcement learning (RL) becoming a key optimization paradigm. However, planning remains a bottleneck, as existing methods struggle with long-horizon strategies. Our analysis reveals a critical phenomenon, plan anchor, where the first reasoning step disproportionately impacts downstream behavior in long-horizon web reasoning tasks. Current RL algorithms, fail to account for this by uniformly distributing rewards across the trajectory. To address this, we propose Anchor-GRPO, a two-stage RL framework that decouples planning and execution. In Stage 1, the agent optimizes its first-step planning using fine-grained rubrics derived from self-play experiences and human calibration. In Stage 2, execution is aligned with the initial plan through sparse rewards, ensuring stable and efficient tool usage. We evaluate Anchor-GRPO on four benchmarks: BrowseComp, BrowseComp-Zh, GAIA, and XBench-DeepSearch. Across models from 3B to 30B, Anchor-GRPO outperforms baseline GRPO and First-step GRPO, improving task success and tool efficiency. Notably, WebAnchor-30B achieves 46.0% pass@1 on BrowseComp and 76.4% on GAIA. Anchor-GRPO also demonstrates strong scalability, getting higher accuracy as model size and context length increase. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03164 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning Yu, Xinmiao Zhang, Liwen Feng, Xiaocheng Jiang, Yong Qin, Bing Xie, Pengjun Zhou, Jingren Computation and Language Large Language Model(LLM)-based agents have shown strong capabilities in web information seeking, with reinforcement learning (RL) becoming a key optimization paradigm. However, planning remains a bottleneck, as existing methods struggle with long-horizon strategies. Our analysis reveals a critical phenomenon, plan anchor, where the first reasoning step disproportionately impacts downstream behavior in long-horizon web reasoning tasks. Current RL algorithms, fail to account for this by uniformly distributing rewards across the trajectory. To address this, we propose Anchor-GRPO, a two-stage RL framework that decouples planning and execution. In Stage 1, the agent optimizes its first-step planning using fine-grained rubrics derived from self-play experiences and human calibration. In Stage 2, execution is aligned with the initial plan through sparse rewards, ensuring stable and efficient tool usage. We evaluate Anchor-GRPO on four benchmarks: BrowseComp, BrowseComp-Zh, GAIA, and XBench-DeepSearch. Across models from 3B to 30B, Anchor-GRPO outperforms baseline GRPO and First-step GRPO, improving task success and tool efficiency. Notably, WebAnchor-30B achieves 46.0% pass@1 on BrowseComp and 76.4% on GAIA. Anchor-GRPO also demonstrates strong scalability, getting higher accuracy as model size and context length increase. |
| title | WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.03164 |