This work re-examines the commonly held assumption that the frequency of rewards is a reliable measure of task difficulty in reinforcement learning. We identify and formalize a structural challenge that undermines the effectiveness of current policy learning methods: when essential subgoals do not directly yield rewards. We characterize such settings as exhibiting zero-incentive dynamics, where transitions critical to success remain unrewarded. We show that state-of-the-art deep subgoal-based algorithms fail to leverage these dynamics and that learning performance is highly sensitive to the temporal proximity between subgoal completion and eventual reward. These findings reveal a fundamental limitation in current approaches and point to the need for mechanisms that can infer latent task structure without relying on immediate incentives.
View on arXiv@article{molinghen2025_2507.01470,
title={ Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals },
author={ Yannick Molinghen and Tom Lenaerts },
journal={arXiv preprint arXiv:2507.01470},
year={ 2025 }
}