This work introduces Slow-OCast, a transfer-learning based model designed for high-resolution ocean environmental forecasting that incorporates the slow-varying motion characteristics of the ocean and comprises two insightful modules.
Qi-Xiu Li, Xiang Zhu, Xiao-Yong Li et al.· Proceedings of the 32nd ACM...· 0 citations
Text summarization requires models to condense content while preserving key qualities such as consistency and coherence. Large language models (LLMs) have shown strong performance on this task and can be further improved through reinforcement learning (RL). However, most existing methods apply reward signals directly to undifferentiated token sequences, overlooking the varying importance of individual tokens to word and sentence level quality in summarization. In this paper, we propose Token Importance-Aware Policy Optimization (TIAO), a novel reinforcement learning strategy that explicitly leverages token-importance awareness. Specifically, TIAO identifies core tokens based on token dependency and reweights a trajectory's advantage according to its overall dependencies. Experiments on the real world dataset show that our TIAO achieves highly competitive results, and that a 7B foundation model enhanced by TIAO performs comparably to GPT-4 and GPT-5-nano. Code is available at https://github.com/TechCloud-x/TIAO
Qi-Xiu Li, Chen-Long Bao, Xiang Zhu et al.· 0 citations
Regional high-resolution ocean environmental forecasting combines spatial numerical modeling with temporal prediction, and is essential for monitoring the ecological security of specific ocean regions. In recent years, deep learning methods are generally more computationally efficient than traditional numerical models and enable fast, accurate forecasting. However, as data resolution increases, the training and computational costs of existing approaches increase substantially. To address this issue, we introduce Slow-OCast, a transfer-learning based model designed for high-resolution ocean environmental forecasting. Specifically, Slow-OCast incorporates the slow-varying motion characteristics of the ocean and comprises two insightful modules. The Fluid Motion Separator that injects low-frequency background dynamics into the fine-tuning process of a foundation model, functioning as a "magnifier" to encode physical priors of ocean dynamics. The Hydrokinetic Energy Path Integrator that provides an implicit representation of flow-field evolution, serving as a "compass" to guide accurate change prediction. We evaluate Slow-OCast on two high-resolution Mediterranean datasets, and results demonstrate Slow-OCast consistently outperforms all baseline methods across forecasting tasks with different lead times.
Qixiu Li, Xiang Zhu, Xiaoyong Li et al.· Proceedings of the 32nd ACM...· 0 citations
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