Anti-Collapse Dynamics and the Emergence of Multi-Time-Scale Learning in Recurrent Neural Networks
arXiv:2606.29519v1 Announce Type: new Abstract: Long-range learning is hard for recurrent networks trained with stochastic gradient descent, because the influence of a past input fades with the lag $\ell$, and if it fades too fast the dependence cannot be learned from finite data. This fade is captured by an envelope $f(\ell)$. An exponential fade makes the data needed to learn a lag-$\ell$ dependence grow exponentially, putting long horizons out of reach; a power-law fade keeps the cost polynomial. We show that the asymptotic decay class of $f(\ell)$ is not fixed by the architecture. Instead,
This paper offers a foundational breakthrough in recurrent neural network capabilities, specifically addressing a long-standing challenge in learning long-range dependencies which has been a significant barrier to advanced AI agent development.
Researchers and developers will find new pathways to building more robust and capable AI systems, extending the practical horizons of AI models that rely on understanding sequential data over long periods.
The asymptotic decay class of how RNNs learn long-range dependencies is no longer seen as fixed by architecture, opening new avenues for designing networks that can learn complex temporal patterns more efficiently.
- · AI researchers
- · Deep learning practitioners
- · AI agent developers
- · Companies building advanced AI models
- · AI architectures less capable of long-range dependencies
Recurrent neural networks will become significantly more effective at tasks requiring long-term memory and sequential data analysis.
This improvement could accelerate the development of more sophisticated AI agents capable of complex planning and understanding broader contexts.
Enhanced long-range learning in AI could lead to breakthroughs in autonomous systems, scientific discovery, and intelligent automation across various sectors.
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Read at arXiv cs.LG