TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.
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The paper proposes message-passing algorithms and a replica theory using cumulant expansion for tensor factorization inference in a dense limit on random graphs, avoiding Gaussian assumptions.
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Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.
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Graphical model for factorization and completion of relatively high rank tensors by sparse sampling
The paper proposes message-passing algorithms and a replica theory using cumulant expansion for tensor factorization inference in a dense limit on random graphs, avoiding Gaussian assumptions.