🤖 AI Summary
This work addresses catastrophic forgetting in continual learning for large language models by proposing a Non-Interfering Weight Field (NIWF) framework, which models model parameters as functions generated from a continuous capability coordinate space. By incorporating anchor snapshots and a functional locking mechanism, the method enables on-demand synthesis of interference-free weight configurations, endowing neural networks for the first time with software-like version control capabilities—supporting commit, composition, extension, and rollback of learned abilities. Experiments on Mistral-7B demonstrate that the approach achieves zero forgetting on previously learned tasks during sequential instruction-following and code generation learning, while maintaining competitive perplexity on new tasks.
📝 Abstract
Large language models store all learned knowledge in a single, fixed weight vector. Teaching a model new capabilities requires modifying those same weights, inevitably degrading previously acquired knowledge. This fundamental limitation, known as catastrophic forgetting, has resisted principled solutions for decades. Existing approaches treat weights as immutable artifacts that must be protected through techniques like regularization heuristics, replay buffers, or isolated adapter modules. The problem is none of these provide a structural guarantee against forgetting. In this work, we propose Non-Interfering Weight Fields (NIWF), a framework that replaces the fixed weight paradigm with a learned function that generates weight configurations on demand from a continuous capability coordinate space. After training on a task, we commit the occupied coordinate region by snapshotting the fields outputs on anchor points to enforce a functional lock during all future training. We validate NIWF on sequential instructionfollowing and code generation tasks using Mistral-7B, demonstrating zero forgetting on committed tasks with competitive perplexity on new tasks. The framework introduces the notion of software-like versioning for neural network intelligence, where capabilities can be committed, extended, composed, and rolled back without retraining.