Unverified50% confidenceFactExact time
The paper focuses on learning rules based on preferential compression and provides tighter results in terms of convergence rate for their risk upper bounds.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
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Related Claims
UnverifiedThis paper provides generalization guarantees under significantly relaxed assumptions, with a lower threshold compared to previous analyses that required strong constraints on the velocity field.70% similarUnverifiedThe paper bridges a long-standing exponential gap between the upper and lower bounds of sample complexity in risk-sensitive reinforcement learning.69% similarUnverifiedThe paper identifies the convergence behavior of empirical risk across different regimes: some regimes maintain an O(1/√N) rate, some regimes converge noticeably slower, and in some regimes the empirical risk no longer exhibits consistency.66% similarUnverifiedThe choice of bottleneck dimensionality is essentially a trade-off between information compression rate and reconstruction quality, corresponding to rate-distortion theory; Shannon proved in 1959 that there exists a theoretical minimum coding rate lower bound for a given distortion tolerance.66% similarUnverifiedToken compression involves trade-offs: some seemingly redundant contextual information may be critical for model reasoning, and aggressive compression may affect output quality in scenarios such as code generation and complex reasoning.65% similar
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