Noise Reinforcement
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Non-Uniform Noise-to-Signal Ratio in the REINFORCE Policy-Gradient Estimator
arXiv:2602.01460v3 Announce Type: replace-cross Abstract: Policy-gradient methods are widely used in reinforcement learning, yet training often becomes unstable or slows down as learning progresses. We study this phenomenon through the noise-to-signal ratio (NSR) of a policy-gradient estimator, defined as the estimator variance (noise) normalized by the squared norm of the true gradient (signal). Our main result is that, for (i) finite-horizon linear systems with Gaussian policies and linear...
Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation
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TetraFuse: A Synergistic Four-Dimensional Dynamic Fusion Framework for Efficient and Robust Medical Image Classification
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Well-Posed KL-Regularized Control via Wasserstein and Kalman-Wasserstein KL Divergences
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SoulsOnly.tff – A font for humans not AI and keyboard firmware to type in it
A font whose rendered glyphs spell readable text while the stored character stream (what copy-paste, HTML/PDF extraction, and scrapers see) is noise. The font is the decoder, applied only at the rendering layer, and the cipher is driven by an ordinary keyboard: you type normal keys, the keyboard emits the noise stream, and only this font renders it back into words. This is a craft and statement project, not a claim of unbreakable security.
Discovering autonomous quantum error correction via deep reinforcement learning
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Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework
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Target Updates May Stabilize Linear Q-Learning: Periodic and Soft Dynamics
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