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Life sciences · Preprint
arXiv · August 14, 2026
Posted before peer review. The findings may change or fail to hold.
This is a preprint describing a novel machine learning method (SAFARI) designed to improve proactive exploration in large language model agents by mitigating hindsight bias and distinguishing productive from redundant exploration. The abstract reports that experiments demonstrate effectiveness but provides no numerical results, effect sizes, or peer review status.
Preprint. Intervention: SAFARI: a method combining Exploratory Data Construction (synthesis of exploration-rich trajectories) and RL Optimization with Contrastive Signal Guidance.
Two fundamental bottlenecks to proactive exploration in LLM agents are identified (specific bottlenecks not detailed in abstract) SAFARI method consists of Exploratory Data Construction and RL Optimization with Contrastive Signal Guidance Extensive experiments demonstrate effectiveness (no quantitative results reported in abstract)
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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This is an unrefereed arXiv preprint presenting a novel machine learning method for training LLM agents, with no peer review or clinical/human outcomes reported.
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We study proactive exploration in LLM agents, i.e., the ability to explore an environment to acquire information that improves future decision-making. In this regard, we first identify two fundamental bottlenecks that hinder this capability and then propose \ours, a novel method designed to instill and refine proactive exploration. Specifically, \ours\ consists of two components: (1) Exploratory Data Construction, which synthesizes exploration-rich trajectories to mitigate the hindsight bias of standard demonstrations; and (2) RL Optimization with Contrastive Signal Guidance, which leverages contrastive trajectory pairs to distinguish productive exploration from redundant wandering. Extensive experiments demonstrate the effectiveness of \ours\ and provide insights into the characteristics of proactive exploration. Our code is available at: https://github.com/GuanZhizhao/SAFARI.
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