Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning
Neural networks are hindered by accumulating dormant neurons and loss of expressivity throughout training, particularly in non-stationary data settings, such as continual supervised and reinforcement learning. Recently, neuron resets have been used to maintain gradient flow and restore plasticity. However, full unit reinitialization often sacrifices peak performance and can destabilize training, leading to policy collapse. To preserve plasticity without destabilizing training, we propose Calibrated Partial Resets (CPR), an optimizer that periodically pulls low-utility neurons toward their initialization, with pull strength scaled by each neuron's utility. Unlike binary reset methods, part...
| Authors | Luc McCutcheon, Evangelos Chatzaroulas, Saber Fallah |
|---|---|
| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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