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ShipTraj-R1: Reinforcing Ship Trajectory Prediction in Large Language Models via Group Relative Policy Optimization

arXiv:2603.02939v1 Announce Type: new Abstract: Recent advancements in reinforcement fine-tuning have significantly improved the reasoning ability of large language models (LLMs). In particular, methods such as group relative policy optimization (GRPO) have demonstrated strong capabilities across various fields. However, applying LLMs to ship trajectory prediction remains largely unexplored. In this paper, we propose ShipTraj-R1, a novel LLM-based framework that reformulates ship trajectory prediction as a text-to-text generation problem. (1) We design a dynamic prompt containing trajectory information about conflicting ships to guide the model to achieve adaptive chain-of-thought (CoT) reasoning. (2) We introduce a comprehensive rule-based reward mechanism to incentivize the reasoning format and prediction accuracy of the model. (3) Our ShipTraj-R1 is reinforced through the GRPO mechanism guided by domain-specific prompts and rewards, and utilizes the Qwen3 as the model backbone. Ext

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Yang Zhan, Yunhao Li, Zhang Chao, Yuxu Lu, Yan Li · · 1 min read · 8 views
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arXiv:2603.02939v1 Announce Type: new Abstract: Recent advancements in reinforcement fine-tuning have significantly improved the reasoning ability of large language models (LLMs). In particular, methods such as group relative policy optimization (GRPO) have demonstrated strong capabilities across various fields. However, applying LLMs to ship trajectory prediction remains largely unexplored. In this paper, we propose ShipTraj-R1, a novel LLM-based framework that reformulates ship trajectory prediction as a text-to-text generation problem. (1) We design a dynamic prompt containing trajectory information about conflicting ships to guide the model to achieve adaptive chain-of-thought (CoT) reasoning. (2) We introduce a comprehensive rule-based reward mechanism to incentivize the reasoning format and prediction accuracy of the model. (3) Our ShipTraj-R1 is reinforced through the GRPO mechanism guided by domain-specific prompts and rewards, and utilizes the Qwen3 as the model backbone. Extensive experimental results on two complex and real-world maritime datasets show that the proposed ShipTraj-R1 achieves the least error compared with state-of-the-art deep learning and LLM-based baselines.

Executive Summary

ShipTraj-R1, a novel large language model (LLM) framework, is proposed to improve ship trajectory prediction. Using a text-to-text generation approach and a dynamic prompt, the model achieves adaptive chain-of-thought reasoning. A comprehensive rule-based reward mechanism and group relative policy optimization (GRPO) reinforce the model's performance. Experimental results on two real-world maritime datasets demonstrate ShipTraj-R1's superiority over state-of-the-art deep learning and LLM-based baselines. This contribution highlights the potential of LLMs in maritime applications and showcases the effectiveness of GRPO in improving model performance.

Key Points

  • ▸ The ShipTraj-R1 framework reformulates ship trajectory prediction as a text-to-text generation problem.
  • ▸ A dynamic prompt and adaptive chain-of-thought reasoning are employed to guide the model.
  • ▸ A comprehensive rule-based reward mechanism and GRPO are used to reinforce the model's performance.

Merits

Strength in Adaptive Reasoning

ShipTraj-R1's use of adaptive chain-of-thought reasoning allows the model to adjust its approach based on the complexity of the input data, leading to improved performance on maritime datasets.

Effective Reinforcement Learning

The combination of GRPO and a comprehensive rule-based reward mechanism effectively incentivizes the model to prioritize prediction accuracy and reasoning format, resulting in superior performance compared to baselines.

Demerits

Limited Dataset Scope

The experimental results are limited to two real-world maritime datasets, and it is unclear how ShipTraj-R1 would perform on other types of datasets or in different maritime scenarios.

Lack of Interpretability

The article does not provide detailed insights into the model's decision-making process, making it challenging to interpret and understand the reasoning behind ShipTraj-R1's predictions.

Expert Commentary

ShipTraj-R1's innovative approach to ship trajectory prediction and its impressive performance on maritime datasets demonstrate the potential of LLMs in this domain. However, the limited dataset scope and lack of interpretability of the model's decision-making process are notable limitations. Further research is necessary to explore the applicability of ShipTraj-R1 in various maritime scenarios and to improve the model's transparency and explainability. The implications of this work are significant, with potential applications in maritime safety and security, and the possibility of influencing policy-making and regulations in this domain.

Recommendations

  • ✓ Future research should focus on expanding the scope of maritime datasets and exploring the performance of ShipTraj-R1 on other types of datasets and in different maritime scenarios.
  • ✓ Developing more transparent and interpretable models, such as those using attention mechanisms or other explainable AI techniques, is necessary to improve the trustworthiness and reliability of ShipTraj-R1 and other LLMs in maritime applications.

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