StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation
Summary: arXiv:2603.23571v1 Announce Type: cross
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.
Key Features of StateLinFormer
- Stateful Memory Mechanism: Unlike traditional models that reset memory states, StateLinFormer maintains continuity, allowing for greater adaptability in navigation tasks.
- Long-Horizon Memory Retention: The model’s architecture facilitates sustained memory across extended interactions, enhancing the navigation capabilities.
- Improved Contextual Adaptation: As the length of interactions increases, the model demonstrates substantial improvements in adapting to context-dependent scenarios.
- Performance Benchmarking: Experiments conducted in both MAZE and ProcTHOR environments reveal that StateLinFormer significantly outperforms both its stateless linear-attention counterpart and standard Transformer models with fixed context windows.
Experimental Results
In various experimental setups, StateLinFormer has shown remarkable performance improvements, particularly in scenarios requiring long-term memory and adaptive learning. The following results were noted:
- MAZE Environment: In this environment, StateLinFormer was able to navigate complex mazes with increased efficiency and accuracy compared to traditional models.
- ProcTHOR Environment: The model excelled in tasks requiring a deep understanding of spatial relationships and long-term planning, showcasing its advanced memory retention capabilities.
- In-Context Learning (ICL): As interaction length increased, StateLinFormer demonstrated a marked improvement in its ICL capabilities, allowing it to better utilize previous experiences for real-time decision-making.
Conclusion
StateLinFormer represents a significant advancement in the field of navigation intelligence, merging the strengths of stateful memory with the efficiency of linear attention mechanisms. By overcoming the limitations of traditional navigation models, it opens new avenues for research and application in complex environments. The ability to maintain and utilize long-term memory effectively positions StateLinFormer as a leading solution for future navigation tasks, paving the way for more intuitive and adaptable AI systems.
Future Directions
The promising results of StateLinFormer encourage further exploration into its capabilities. Future research may focus on:
- Enhancing the model’s adaptability in even more complex environments.
- Integrating additional sensory inputs to improve navigation performance.
- Exploring the implications of stateful training in other AI domains beyond navigation.