Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
Overview
Overall Novelty Assessment
The paper proposes RLDP, a method that revisits next-state prediction in latent space for learning state features in zero-shot reinforcement learning, augmented with a regularization to prevent feature collapse. It resides in the 'Next-State Prediction and Latent Dynamics Modeling' leaf, which contains only two papers (including this one), indicating a relatively sparse research direction within the broader 'Latent Dynamics and Predictive Representation Learning' branch. This positioning suggests the paper addresses a focused niche: simple predictive objectives for zero-shot RL, rather than the more crowded contrastive or meta-learning approaches elsewhere in the taxonomy.
The taxonomy reveals that neighboring branches emphasize alternative strategies: 'Temporal Difference and Forward-Backward Representations' (four papers) explores TD-based or bidirectional dynamics, while 'Contrastive and Self-Supervised Representation Learning' (six papers across two leaves) prioritizes contrastive losses over temporal prediction. The paper's focus on next-state prediction with regularization diverges from these directions by questioning whether complex objectives are necessary, positioning it as a simplification or baseline challenge to methods in adjacent branches that employ more elaborate contrastive or invariance-based objectives.
Among the three contributions analyzed, the core RLDP method and its robustness in low-coverage settings each examined ten candidates with no clear refutations, suggesting these aspects may be relatively novel within the limited search scope of thirty papers. However, the identification and mitigation of feature collapse examined ten candidates and found three that appear to provide overlapping prior work, indicating this specific problem and its solution have been addressed to some extent in the examined literature. The analysis does not claim exhaustive coverage, so additional relevant work may exist beyond the top-30 semantic matches.
Given the limited search scope and the paper's position in a sparse taxonomy leaf, the work appears to offer a focused contribution by revisiting a simple objective with a targeted fix for feature collapse. The analysis suggests moderate novelty for the method and robustness claims, while the feature collapse insight has more substantial prior overlap among the candidates examined. A broader literature search would be needed to assess whether the simplicity argument and regularization approach represent a significant departure from state-of-the-art complex methods.
Taxonomy
Research Landscape Overview
Claimed Contributions
The authors introduce RLDP, a representation learning method that combines latent next-state prediction with orthogonal regularization to prevent feature collapse. This approach provides a simpler alternative to complex successor measure estimation methods while maintaining competitive performance for zero-shot reinforcement learning.
The authors identify that naive latent dynamics prediction leads to increasing state-feature similarity (a mild form of feature collapse) that reduces the span of representable reward functions. They propose orthogonal regularization as a solution to maintain feature diversity during representation learning.
The authors demonstrate that RLDP, being a policy-independent representation learning objective, succeeds in low-coverage scenarios where prior approaches that rely on explicit Bellman backups struggle due to out-of-distribution action selection issues.
Core Task Comparisons
Comparisons with papers in the same taxonomy category
[12] Towards Robust Zero-Shot Reinforcement Learning PDF
Contribution Analysis
Detailed comparisons for each claimed contribution
Regularized Latent Dynamics Prediction (RLDP) method
The authors introduce RLDP, a representation learning method that combines latent next-state prediction with orthogonal regularization to prevent feature collapse. This approach provides a simpler alternative to complex successor measure estimation methods while maintaining competitive performance for zero-shot reinforcement learning.
[9] Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL PDF
[29] Zero-shot whole-body humanoid control via behavioral foundation models PDF
[51] Prototypical context-aware dynamics for generalization in visual control with model-based reinforcement learning PDF
[52] DRED: Zero-shot transfer in reinforcement learning via data-regularised environment design PDF
[53] Empowering Aerial Maneuver Games Through Model-Based Constrained Reinforcement Learning PDF
[54] Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space PDF
[55] Kitchenshift: Evaluating zero-shot generalization of imitation-based policy learning under domain shifts PDF
[56] Sim-to-real via latent prediction: Transferring visual non-prehensile manipulation policies PDF
[57] RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues PDF
[58] Transfer RL across observation feature spaces via model-based regularization PDF
Identification and mitigation of feature collapse in latent dynamics prediction
The authors identify that naive latent dynamics prediction leads to increasing state-feature similarity (a mild form of feature collapse) that reduces the span of representable reward functions. They propose orthogonal regularization as a solution to maintain feature diversity during representation learning.
[46] On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning PDF
[68] Understanding self-predictive learning for reinforcement learning PDF
[75] Quantized Representations Prevent Dimensional Collapse in Self-predictive RL PDF
[10] TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning PDF
[69] Constrained latent action policies for model-based offline reinforcement learning PDF
[70] Analyzing and overcoming degradation in warm-start reinforcement learning PDF
[71] A reliable representation with bidirectional transition model for visual reinforcement learning generalization PDF
[72] Derl: Coupling decomposition in action space for reinforcement learning task PDF
[73] Ilpo-mp: Mode priors prevent mode collapse when imitating latent policies from observations PDF
[74] Sim2real transfer for deep reinforcement learning with stochastic state transition delays PDF
Demonstration of robustness in low-coverage settings
The authors demonstrate that RLDP, being a policy-independent representation learning objective, succeeds in low-coverage scenarios where prior approaches that rely on explicit Bellman backups struggle due to out-of-distribution action selection issues.