Thinking While Listening: Fast-Slow Recurrence for Long-Horizon Sequential Modeling
arXiv:2604.01577v1 Announce Type: new Abstract: We extend the recent latent recurrent modeling to sequential input streams. By interleaving fast, recurrent latent updates with self-organizational ability between slow observation updates, our method facilitates the learning of stable internal structures that evolve alongside the input. This mechanism allows the model to maintain coherent and clustered representations over long horizons, improving out-of-distribution generalization in reinforcement learning and algorithmic tasks compared to sequential baselines such as LSTM, state space models, and Transformer variants.
arXiv:2604.01577v1 Announce Type: new Abstract: We extend the recent latent recurrent modeling to sequential input streams. By interleaving fast, recurrent latent updates with self-organizational ability between slow observation updates, our method facilitates the learning of stable internal structures that evolve alongside the input. This mechanism allows the model to maintain coherent and clustered representations over long horizons, improving out-of-distribution generalization in reinforcement learning and algorithmic tasks compared to sequential baselines such as LSTM, state space models, and Transformer variants.
Executive Summary
The article introduces a novel latent recurrent modeling framework that integrates fast recurrent updates with slow observation updates to enhance long-horizon sequential modeling. By enabling stable internal structures to evolve alongside input streams, the method improves out-of-distribution generalization in reinforcement learning and algorithmic tasks. This hybrid temporal resolution approach appears to address limitations of conventional models like LSTM, state space models, and Transformer variants, particularly in maintaining coherent representations over extended sequences.
Key Points
- ▸ Integration of fast-slow recurrence for improved sequential modeling
- ▸ Enhanced out-of-distribution generalization via stable internal structures
- ▸ Addressing limitations of existing sequential models in long-horizon contexts
Merits
Innovation in Temporal Resolution
The combination of fast and slow recurrence introduces a new paradigm for capturing both rapid and gradual changes in sequential data, offering a more nuanced modeling capability.
Demerits
Complexity Trade-off
The interleaving of fast and slow updates may increase computational complexity and training difficulty, potentially affecting scalability for large-scale applications.
Expert Commentary
This work represents a significant advancement in the field of sequential modeling by proposing a hybrid temporal mechanism that balances rapid and slow dynamics. The authors effectively identify a critical gap in current models—namely, the inability to maintain coherent representations over long horizons under varying input conditions. Their solution, which leverages a fast-slow recurrence paradigm to preserve internal structure, is both theoretically grounded and empirically promising. While the increased complexity may pose challenges for deployment, the potential gains in generalization and representational stability justify further exploration. This paper contributes meaningfully to the broader discourse on modeling temporal dependencies, particularly for applications where long-term coherence is paramount.
Recommendations
- ✓ Further empirical validation across diverse reinforcement learning environments to quantify generalization gains
- ✓ Consideration of hybrid architectures that allow configurable fast-slow trade-offs for application-specific adaptability
Sources
Original: arXiv - cs.LG
