Academic

StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation

arXiv:2603.23571v1 Announce Type: new Abstract: Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular systems rely on explicit mapping but lack flexibility, while Transformer-based end-to-end models are constrained by fixed context windows, limiting persistent memory across extended interactions. We introduce StateLinFormer, a linear-attention navigation model trained with a stateful memory mechanism that preserves recurrent memory states across consecutive training segments instead of reinitializing them at each batch boundary. This training paradigm effectively approximates learning on infinitely long sequences, enabling the model to achieve long-horizon memory retention. Experiments across both MAZE and ProcTHOR environments demonstrate that StateLinFormer significantly outperforms its stateless linear-attention counterpart and standard Transformer baseline

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Zhiyuan Chen, Yuxuan Zhong, Fan Wang, Bo Yu, Pengtao Shao, Shaoshan Liu, Ning Ding · · 1 min read · 6 views
#cs.LG #cs.AI

arXiv:2603.23571v1 Announce Type: new Abstract: Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular systems rely on explicit mapping but lack flexibility, while Transformer-based end-to-end models are constrained by fixed context windows, limiting persistent memory across extended interactions. We introduce StateLinFormer, a linear-attention navigation model trained with a stateful memory mechanism that preserves recurrent memory states across consecutive training segments instead of reinitializing them at each batch boundary. This training paradigm effectively approximates learning on infinitely long sequences, enabling the model to achieve long-horizon memory retention. Experiments across both MAZE and ProcTHOR environments demonstrate that StateLinFormer significantly outperforms its stateless linear-attention counterpart and standard Transformer baselines with fixed context windows. Notably, as interaction length increases, persistent stateful training substantially improves context-dependent adaptation, suggesting an enhancement in the model's In-Context Learning (ICL) capabilities for navigation tasks.

Executive Summary

This article introduces StateLinFormer, a novel linear-attention navigation model that leverages a stateful memory mechanism to enhance long-term memory retention in navigation tasks. By preserving recurrent memory states across consecutive training segments, StateLinFormer effectively approximates learning on infinitely long sequences, outperforming its stateless linear-attention counterpart and standard Transformer baselines in both MAZE and ProcTHOR environments. The model's ability to adapt to context-dependent interactions, particularly at extended lengths, significantly improves In-Context Learning (ICL) capabilities. The findings suggest that StateLinFormer's stateful training paradigm can be a valuable approach for navigation intelligence, where long-term memory is crucial for both immediate generalization and sustained adaptation.

Key Points

  • ▸ StateLinFormer introduces a stateful memory mechanism to enhance long-term memory retention in navigation tasks.
  • ▸ The model preserves recurrent memory states across consecutive training segments to approximate learning on infinitely long sequences.
  • ▸ StateLinFormer outperforms its stateless linear-attention counterpart and standard Transformer baselines in both MAZE and ProcTHOR environments.

Merits

Enhanced Long-term Memory Retention

StateLinFormer's stateful memory mechanism enables the model to retain long-term memory, improving its ability to adapt to context-dependent interactions and achieve sustained adaptation in navigation tasks.

Demerits

Training Complexity

StateLinFormer's stateful training paradigm may introduce additional complexity in terms of training and implementation, which could pose challenges for large-scale deployment and practical applications.

Expert Commentary

The introduction of StateLinFormer marks a significant advancement in navigation intelligence, as it effectively addresses the dilemma of modular systems and Transformer-based end-to-end models. By leveraging a stateful memory mechanism, StateLinFormer achieves long-horizon memory retention and improved In-Context Learning (ICL) capabilities, outperforming its baselines in both MAZE and ProcTHOR environments. The article's findings have far-reaching implications for the development of more effective AI-powered navigation systems, particularly in applications where sustained adaptation is crucial. However, the increased training complexity of StateLinFormer's stateful training paradigm should be addressed to ensure practical scalability and deployment.

Recommendations

  • ✓ Future research should focus on exploring the applicability of StateLinFormer's stateful memory mechanism to other AI-powered navigation systems, such as those relying on graph-based representations or attention-based architectures.
  • ✓ Developers should investigate methods to mitigate the training complexity of StateLinFormer, potentially through the use of more efficient optimization algorithms or data augmentation techniques.

Sources

Original: arXiv - cs.LG

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