Curiosity-Critic rewards the improvement in cumulative prediction error via a tractable per-step surrogate (current error minus learned asymptotic baseline), outperforming prior curiosity methods in a stochastic grid world.
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
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Refined probabilistic and smooth l0 pruning techniques approximate minimum description length for neural networks, achieving high compression with minimal accuracy loss and empirically verifying better sample efficiency and generalization on image and text tasks.
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Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training
Curiosity-Critic rewards the improvement in cumulative prediction error via a tractable per-step surrogate (current error minus learned asymptotic baseline), outperforming prior curiosity methods in a stochastic grid world.
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Efficient compression of neural networks and datasets
Refined probabilistic and smooth l0 pruning techniques approximate minimum description length for neural networks, achieving high compression with minimal accuracy loss and empirically verifying better sample efficiency and generalization on image and text tasks.