# Exploring Learnability in Memory-Augmented Recurrent Neural Networks: Precision, Stability, and Empirical Insights

arXiv: https://arxiv.org/abs/2410.03154 · HTML: https://mlsanity.com/paper/2410.03154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Exploring Length Generalization in Large Language Models](https://mlsanity.com/paper/2207.04901/) (2022)
- [On the Turing Completeness of Modern Neural Network Architectures](https://mlsanity.com/paper/1901.03429/) (2019)
- [Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets](https://mlsanity.com/paper/1503.01007/) (2015)
- [A Formal Hierarchy of RNN Architectures](https://mlsanity.com/paper/2004.08500/) (2020)
- [Learning to Transduce with Unbounded Memory](https://mlsanity.com/paper/1506.02516/) (2015)
- [Associative Long Short-Term Memory](https://mlsanity.com/paper/1602.03032/) (2016)
- [Neural Random-Access Machines](https://mlsanity.com/paper/1511.06392/) (2015)
- [Recurrent Neural Networks as Weighted Language Recognizers](https://mlsanity.com/paper/1711.05408/) (2017)
- [Minimum Description Length Recurrent Neural Networks](https://mlsanity.com/paper/2111.00600/) (2021)
- [A Survey of Neural Networks and Formal Languages](https://mlsanity.com/paper/2006.01338/) (2020)
- [Evaluating the Ability of LSTMs to Learn Context-Free Grammars](https://mlsanity.com/paper/1811.02611/) (2018)
- [Learning Context-Free Languages with Nondeterministic Stack RNNs](https://mlsanity.com/paper/2010.04674/) (2020)
- [The Neural State Pushdown Automata](https://mlsanity.com/paper/1909.05233/) (2019)
- [Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units](https://mlsanity.com/paper/2104.02899/) (2021)
- [On the Computational Complexity and Formal Hierarchy of Second Order Recurrent Neural Networks](https://mlsanity.com/paper/2309.14691/) (2023)
- [Investigating Symbolic Capabilities of Large Language Models](https://mlsanity.com/paper/2405.13209/) (2024)
- [The Surprising Computational Power of Nondeterministic Stack RNNs](https://mlsanity.com/paper/2210.01343/) (2022)

## Abstract (arXiv)

This study explores the learnability of memory-less and memory-augmented RNNs, which are theoretically equivalent to Pushdown Automata. Empirical results show that these models often fail to generalize on longer sequences, relying more on precision than mastering symbolic grammar. Experiments on fully trained and component-frozen models reveal that freezing the memory component significantly improves performance, achieving state-of-the-art results on the Penn Treebank dataset (test perplexity reduced from 123.5 to 120.5). Models with frozen memory retained up to 90% of initial performance on longer sequences, compared to a 60% drop in standard models. Theoretical analysis suggests that freezing memory stabilizes temporal dependencies, leading to robust convergence. These findings stress the need for stable memory designs and long-sequence evaluations to understand RNNs true learnability limits.