On the Structural Limitations of Weight-Based Neural Adaptation and the Role of Reversible Behavioral Learning

TL;DR

This paper explores the limitations of weight-based neural adaptation, introduces the concept of structural irreversibility, and proposes reversible behavioral learning to enable deterministic rollback of model behavior.

Contribution

It introduces the concept of structural irreversibility and proposes reversible behavioral learning, allowing deterministic unloading of behaviors without parameter resets.

Findings

Reversible learning achieves near-perfect rollback within numerical precision.

Shared-parameter mutation causes persistent divergence from original behavior.

Recoverability Factor quantifies behavioral recoverability.

Abstract

Neural models are usually adapted through changes in parameters shared among model components via fine-tuning, alignment-based training, and reinforcement learning. These changes have been found effective in short-term optimization. However, they result in long-term alterations in the model's base behavior. In this study, we introduce the concept of structural irreversibility as a characteristic of shared-parameter model adaptation. This concept refers to the intertwining of task-specific objectives with the representational identity of the model. We show that when parameters are directly mutated, the resulting model behaves divergently from the original model. This divergence cannot be reversed deterministically without an explicit parameter snapshot. We introduce reversible behavioral learning, in which model behaviors are structurally dissociated from identity parameters and can be deterministically unloaded through an explicit unload process. We also introduce the Recoverability Factor as a normalized measure of behavioral recoverability and provide additional diagnostics based on model divergence. Experiments show that reversible model adaptation achieves rollback within numerical precision, whereas shared-parameter mutation exhibits persistent post-reset divergence.