Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals

Yannick Molinghen; Tom Lenaerts

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arXiv · 2507.01470

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals

Yannick Molinghen·Tom Lenaerts

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Abstract

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.

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Yannick Molinghen, Tom Lenaerts. 2025-07-02. Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals. https://arxiv.org/abs/2507.01470

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