Safe Online Adaptive Refinement (SOAR-Flow), a framework for safe and stable online fine-tuning of offline-initialized Flow-based driving policies, introduces Gaussian-return-aware value calibration based on calibrated Q-learning (Cal-QL), to support stable policy updates.
Ze-Qiao Li, Yi-Jing Wang, Hao-Yu Wang et al.· 0 citations
Distributional critics model the full return distribution, providing richer signals for exploration and risk-sensitive control. Recent work has shown that flow-based critics can model these distributions flexibly and provide effective supervision across flow time for temporal-difference learning. However, unlike standa...
Ze-Qiao Li, Yi-Jing Wang, Hao-Yu Wang et al.· 0 citations
The proposed fusion-boundary-aligned routing regulates each modality's contribution before the first learned cross-modal feature-value mixing operation, supported by Spearman correlations between the learned routing weights and model-specific leave-one-modality-out utility range from 0.45 to 0.66.
Hai-Fan Zhang, Yijing Wang, Haoyu Wang et al.· 0 citations
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