| title | SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating |
|---|---|
| authors | Zequn Xie, Junjie Wang, Dan Yang, Jie Feng, Yue Shen, Jian Wang, Jinjie Gu |
| date | 2026-06-05 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.07074 |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this power comes at a steep computational cost. Driven by accuracy-focused training paradigms, current models adopt brute-force strategies characterized by blind tool dependency and performative reasoning-generating long, redundant trajectories that are far from necessary for resolving these tasks, leading to wasteful tool calls and excessive token consumption. To overcome this efficiency trap, we propose SlimSearcher, a principled framework that pushes the Pareto frontier between accuracy and computational cost across both Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). In the SFT stage, SlimSearcher employs Pareto-efficient filtration to distill trajectories that are both successful and economical, guiding the model toward inherently efficiency-aware search behaviors. During RL, we introduce Adaptive Reward Gating, a dynamic reward-shaping mechanism that evaluates relative tool and token efficiency within a sampled cohort. By cascading these adaptive efficiency metrics with a strict correctness gate, our approach effectively avoids the brevity bias associated with absolute penalties and mitigates reward hacking. Extensive experiments on long-horizon benchmarks, including GAIA, BrowseComp, and XBenchDeepSearch, demonstrate that SlimSearcher reduces average tool-call rounds by 17%-58% while maintaining or improving accuracy. |
cite
@misc{xie2026slimsearcher,
title={SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating},
author={Zequn Xie and Junjie Wang and Dan Yang and Jie Feng and Yue Shen and Jian Wang and Jinjie Gu},
year={2026},
eprint={2606.07074},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.07074}
}