Failing to Explore: Language Models on Interactive Tasks

Mahdi JafariRaviz, Keivan Rezaei, Arshia Soltani Moakhar, Zahra Sodagar, Yize Cheng, Soheil Feizi

Abstract: We evaluate language models on their ability to explore interactive environments under a limited interaction budget. We introduce three parametric tasks with controllable exploration difficulty, spanning continuous and discrete environments. Across state-of-the-art models, we find systematic under-exploration and suboptimal solutions, with performance often significantly worse than simple explore--exploit heuristic baselines and scaling weakly as the budget increases. Finally, we study two lightweight interventions: splitting a fixed budget into parallel executions, which surprisingly improves performance despite a no-gain theoretical result for our tasks, and periodically summarizing the interaction history, which preserves key discoveries and further improves exploration.

External IDs:dblp:journals/corr/abs-2601-22345