Generalization in offline RL: The structure is more important than the amount of pessimism

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Mathematics & Computational Sciences · Depth: Expert, medium

Summary

This paper challenges the conventional wisdom that mild pessimism is crucial for generalization in offline reinforcement learning (RL). It demonstrates that optimal generalization in contextual MDPs (CMDPs) depends not on the amount of pessimism, but on whether its structure respects the underlying symmetries of the optimal solution. The authors prove that a mildly pessimistic, non-symmetric value function can generalize worse than an overly pessimistic, symmetric one. Since dataset coverage determines pessimism's structure in offline RL, enforcing symmetric value functions might require techniques like data augmentation (DA). Inspired by this theory, the paper proposes applying DA through a consistency loss during policy extraction, contrasting with the common practice of regular offline training on augmented datasets. This approach is empirically validated using IQL and CQL on a rotationally symmetric reacher environment, showing improved generalization.

Key takeaway

For Machine Learning Engineers optimizing offline RL generalization, you should prioritize the structural symmetry of your learned value functions over simply minimizing pessimism. Implement data augmentation techniques, specifically through a consistency loss during policy extraction, to enforce these symmetries. This approach is empirically shown to yield superior zero-shot policy transfer in new environments compared to traditional methods of training on augmented datasets, directly impacting your model's robustness and adaptability.

Key insights

Generalization in offline RL hinges on pessimistic structure respecting optimal solution symmetries, not just pessimism amount.

Principles

• Pessimism's structure, not its amount, is key for offline RL generalization.
• Symmetric value functions enable optimal generalization even with high pessimism.
• Dataset coverage directly influences the structure of learned pessimism.

Method

Apply data augmentation via a consistency loss during policy extraction to enforce symmetric value functions, rather than solely augmenting datasets for offline training.

In practice

• Implement data augmentation with consistency loss for policy extraction.
• Evaluate offline RL algorithms (IQL, CQL) in symmetric environments.
• Prioritize value function symmetry over accuracy for generalization.

Topics

• Offline Reinforcement Learning
• Generalization
• Pessimism Structure
• Data Augmentation
• Symmetry Groups
• Contextual MDPs

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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