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A commentary on Nick Cammarata's Goodfire essay 'Neural Networks Are Already Clean' exploring whether a sparse 'semantic skeleton' of bipolar neurons plus external VSA hypervector memory could enable continual learning with small frozen models, while noting obstacles like tangled fact-routing and coordinate-registry alignment.
This paper introduces Vector-Symbolic Policy Gradient (VSPG), a novel method that uses vector symbolic architecture for discrete-action policy gradients in reinforcement learning, achieving competitive performance with robust degradation under noise for edge systems.