FIRE: Frobenius-Isometry Reinitialization for Balancing the Stability-Plasticity Tradeoff
Summary: arXiv:2602.08040v3
Announce Type: replace-cross
Abstract: Deep neural networks trained on nonstationary data must balance stability (i.e., retaining prior knowledge) and plasticity (i.e., adapting to new tasks). Standard reinitialization methods, which reinitialize weights toward their original values, are widely used but difficult to tune: conservative reinitializations fail to restore plasticity, while aggressive ones erase useful knowledge. We propose FIRE, a principled reinitialization method that explicitly balances the stability-plasticity tradeoff.
Introduction
The challenge of training deep neural networks on nonstationary data is significant, as these models must maintain a delicate balance between two opposing forces: stability and plasticity. Stability allows a model to retain previously learned information, while plasticity enables it to adapt to new tasks. Finding this balance is crucial for the effective performance of neural networks.
Current Reinitialization Methods
Standard reinitialization techniques often involve adjusting the weights of a neural network toward their initial values. However, these methods can be problematic. The tuning of conservative reinitializations may lead to inadequate plasticity restoration, whereas more aggressive approaches tend to overwrite valuable knowledge. Consequently, researchers have sought a more effective strategy.
Introducing FIRE
FIRE, or Frobenius-Isometry Reinitialization, addresses the limitations of traditional methods by providing a systematic approach to reinitialization. The key innovations of FIRE include:
- Squared Frobenius Error (SFE): This metric quantifies stability by measuring the proximity of current weights to past weights.
- Deviation from Isometry (DfI): This metric captures plasticity by reflecting the isotropy of the weights.
FIRE’s reinitialization process is derived from a constrained optimization problem, which aims to minimize the SFE while ensuring that the DfI remains zero. This optimization is efficiently approximated using Newton-Schulz iteration methods.
Evaluation and Results
The effectiveness of FIRE was rigorously evaluated across several domains including:
- Continual Visual Learning: FIRE was tested using the CIFAR-10 dataset with a ResNet-18 architecture.
- Language Modeling: The model was applied to OpenWebText using GPT-0.1B.
- Reinforcement Learning: FIRE was assessed on the HumanoidBench using Soft Actor-Critic (SAC) and on Atari games utilizing Deep Q-Networks (DQN).
Across all tested domains, FIRE consistently outperformed both naive training methods and traditional reinitialization techniques. This demonstrates its ability to effectively balance the stability-plasticity tradeoff, enhancing the neural network’s performance in dynamic environments.
Conclusion
FIRE represents a significant advancement in the field of deep learning, particularly for applications involving nonstationary data. By providing a robust framework for reinitialization, it allows neural networks to maintain stability while remaining adaptable. Future research and applications of FIRE could further enhance the capabilities of artificial intelligence systems across various domains.