Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning

2026-05-22 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, short

Summary

A new theoretical model for Out-of-Distribution (OOD) generalization in Reinforcement Learning (RL) agents is presented by Nasehatul Mustakim and Lucas Lehnert in their paper submitted on May 19, 2026. Their work extends the existing state abstraction framework to Partially Observable Markov Decision Processes (POMDPs). They define a novel "successor-weighted model reduction" technique, which compresses abstract state spaces more effectively than previous methods. The authors derive a performance bound for OOD generalization, demonstrating that reducing an agent's abstract state space size directly enhances test performance and OOD generalization. This analysis suggests that constraining RL agents to a small, finite set of abstract states is crucial for achieving generalization across tasks of varying complexity.

Key takeaway

For AI Scientists developing Reinforcement Learning systems that need to perform robustly in novel, complex environments, you should prioritize designing architectures that explicitly constrain and reduce the abstract state space. This approach is theoretically shown to be necessary for cross-scale generalization, directly improving Out-of-Distribution performance. Consider integrating successor-weighted model reduction or similar compression techniques into your RL agent designs to enhance adaptability.

Key insights

Constraining RL agents to smaller abstract state spaces is crucial for achieving Out-of-Distribution generalization across complex tasks.

Principles

• OOD generalization requires abstract state space reduction.
• Smaller abstract spaces improve RL test performance.
• Abstraction functions are key for equivalent experiences.

Method

The paper extends state abstraction to POMDPs and introduces "successor-weighted model reduction" to compress abstract spaces. It then derives an OOD test performance bound.

In practice

• Focus RL architecture design on state compression.
• Implement successor-weighted model reduction.

Topics

• Reinforcement Learning
• Out-of-Distribution Generalization
• State Abstraction
• Partially Observable Markov Decision Processes
• Model Reduction
• Cross-Scale Generalization

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.