<ul><li>Researchers have studied the approximation capabilities and convergence behaviors of one-layer transformers for in-context reasoning and next-token prediction tasks.</li><li>The research addressed gaps in theoretical understanding by proving the Bayes optimality of certain one-layer transformers with linear and ReLU attention.</li><li>Through finite-sample analysis, it was shown that the expected loss of these transformers converges at a linear rate to the Bayes risk during training with gradient descent.</li><li>The study also demonstrated that the trained models generalize well to unseen samples and exhibit expected learning behaviors based on empirical observations from previous works.</li></ul>

One-Layer Transformers are Provably Optimal for In-context Reasoning and Distributional Association Learning in Next-Token Prediction Tasks

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