TeLAPA maintains archives of behaviorally diverse yet competent policies aligned in a shared latent space to preserve plasticity and enable faster recovery after interference in continual reinforcement learning.
Maintaining plasticity in continual learning via regenerative regularization
5 Pith papers cite this work. Polarity classification is still indexing.
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RPSFT improves the in-domain versus out-of-domain performance trade-off during LLM supervised fine-tuning by penalizing rotations in pretrained singular subspaces as a proxy for loss-sensitive directions.
This paper defines loss of plasticity via stable manifolds in parameter space and identifies frozen units and cloned-unit manifolds as the main mechanisms that trap gradient trajectories in non-stationary settings.
Newborn units in growing neural networks are forward-active but backward-starved, receiving weaker gradients than existing units and creating integration challenges that make growth less reliable than pruning in complex tasks.
Smooth-Leaky and Randomized Smooth-Leaky activations mitigate loss of plasticity in continual learning by targeting negative-branch shape and saturation behavior.
citing papers explorer
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Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
TeLAPA maintains archives of behaviorally diverse yet competent policies aligned in a shared latent space to preserve plasticity and enable faster recovery after interference in continual reinforcement learning.
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Rotation-Preserving Supervised Fine-Tuning
RPSFT improves the in-domain versus out-of-domain performance trade-off during LLM supervised fine-tuning by penalizing rotations in pretrained singular subspaces as a proxy for loss-sensitive directions.
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Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity
This paper defines loss of plasticity via stable manifolds in parameter space and identifies frozen units and cloned-unit manifolds as the main mechanisms that trap gradient trajectories in non-stationary settings.
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On the Stability of Growth in Structural Plasticity
Newborn units in growing neural networks are forward-active but backward-starved, receiving weaker gradients than existing units and creating integration challenges that make growth less reliable than pruning in complex tasks.
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Activation Function Design Sustains Plasticity in Continual Learning
Smooth-Leaky and Randomized Smooth-Leaky activations mitigate loss of plasticity in continual learning by targeting negative-branch shape and saturation behavior.