mlsanityData to 7 Oct 2026

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

arXiv 2603.02939 ↗First posted 3 Mar 2026cs.AIcited by 1

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 13 of its 27 references that are in the map. First posted on arXiv 3 Mar 2026.

Builds on

13 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan

Other references · 12

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2025Qwen3 Technical Reportcited by 8,195
  4. 2024Qwen2 Technical Reportcited by 2,076
  5. 2023Baichuan 2: Open Large-scale Language Modelscited by 664
  6. 2025VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Modelcited by 515
  7. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  8. 2025HunyuanImage 3.0 Technical Reportcited by 138
  9. 2024SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Modelcited by 112
  10. 2024LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Modelscited by 35
  11. 2024Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Predictioncited by 31
  12. 2024LG-Traj: LLM Guided Pedestrian Trajectory Predictioncited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026UAVBench and UAVIT-1M: Benchmarking and Enhancing MLLMs for Low-Altitude UAV Vision-Language Understandingcited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2026Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
  2. 2026Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction
  3. 2026Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios
  4. 2025Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs
  5. 2026ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning
  6. 2025Navigation-GPT: A Robust and Adaptive Framework Utilizing Large Language Models for Navigation Applications
  7. 2024Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning
  8. 2025Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey