A betweenness centrality for stochastic networks is defined via an absorbing Markov chain on sequences of reported central nodes, with importance given by pre-absorption occupancy and estimated by Monte Carlo.
Integral Probability Metrics and Their Generating Classes of Functions , volume=
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FEST improves RLVR sample efficiency on math and coding benchmarks by combining supervised signals, on-policy signals, and decaying weights on just 128 randomly chosen demonstrations, matching full-dataset baselines.
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Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach
A betweenness centrality for stochastic networks is defined via an absorbing Markov chain on sequences of reported central nodes, with importance given by pre-absorption occupancy and estimated by Monte Carlo.
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Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance
FEST improves RLVR sample efficiency on math and coding benchmarks by combining supervised signals, on-policy signals, and decaying weights on just 128 randomly chosen demonstrations, matching full-dataset baselines.