R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning

• URL: http://arxiv.org/abs/2605.14026v1
• Date: Wed, 13 May 2026 18:38:32 GMT
• Title: R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
• Abstract summary: We propose Robust Representation via Redundancy Reduction (R2R2) as a regularization method within Self-Predictive Learning (SPL)<n>We verify R2R2 on SPL-native algorithms like TD7.<n> Experiments across 11 continuous control tasks confirm that R2R2 effectively mitigates overfitting.
• Score: 40.03346193264488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For reinforcement learning in data-scarce domains like real-world robotics, intensive data reuse enhances efficiency but induces overfitting. While prior works focus on critic bias, representation-level instability in Self-Predictive Learning (SPL) under high Update-to-Data (UTD) regimes remains underexplored. To bridge this gap, we propose Robust Representation via Redundancy Reduction (R2R2), a regularization method within SPL. We theoretically identify that standard zero-centering conflicts with SPL's spectral properties and design a non-centered objective accordingly. We verify R2R2 on SPL-native algorithms like TD7. Furthermore, to demonstrate its orthogonality to prior advancements, we extend the state-of-the-art SimbaV2, which originally lacks SPL, by integrating a tailored SPL module, termed SimbaV2-SPL. Experiments across 11 continuous control tasks confirm that R2R2 effectively mitigates overfitting; specifically, at a UTD ratio of 20, it improves TD7 by ~22% and provides additional gains on top of SimbaV2-SPL, which itself establishes a new state-of-the-art. The code can be found at: https://github.com/songsang7/R2R2

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