mlsanityData to 14 Oct 2026

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

arXiv 2507.01470 ↗First posted 2 Jul 2025cs.LG cs.AIcited by 1

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 32 references that are in the map. First posted on arXiv 2 Jul 2025.

Builds on

14 of 32 references.

References · 14

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2016Concrete Problems in AI Safetycited by 1,642
  3. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  4. 2017Hindsight Experience Replaycited by 1,136
  5. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  6. 2019The StarCraft Multi-Agent Challengecited by 597
  7. 2016#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learningcited by 309
  8. 2020QPLEX: Duplex Dueling Multi-Agent Q-Learningcited by 270
  9. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
  10. 2019Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewardscited by 78
  11. 2022MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffercited by 34
  12. 2019Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learningcited by 33
2 more
  1. 2021HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanismcited by 19
  2. 2024Laser Learning Environment: A new environment for coordination-critical multi-agent taskscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Certifying cooperation: a novel approach to cooperative multi-agent task generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Zero Collapse: A Failure Mode of Policy Gradient Methods in Discontinuous Reward Environments
  2. 2025Reward Models in Deep Reinforcement Learning: A Survey
  3. 2024Effective Reward Specification in Deep Reinforcement Learning
  4. 2026On Training in Imagination
  5. 2026Beyond Distribution Sharpening: The Importance of Task Rewards
  6. 2026Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness
  7. 2026Enhancing VLM Reward Models Through Structure-Aware Fine-Tuning
  8. 2026Do LLMs Learn from Rewards in Context? : Rethinking the role of reward in In-Context Reinforcement Learning