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Bayesian fine-tuning experiments with LLMs and vision-language models support the proposed state-dependent heavy-tailed oracle model and show that RA-LMC matches or outperforms state-of-the-art baselines in accuracy and uncertainty quantification.
We study stochastic approximation of fixed points of a non-expansive operator $T$ when the oracle samples originate from a continuing Markovian trajectory. A direct block-minibatch implementation of Halpern iteration attains an expected last-iterate residual of order $O(\log N/N)$, but accrues a substantive complexity...
A nonparametric distributional Bellman optimality operator for JMDPs is defined, and it is proved that when the induced marginal MDP has a unique optimal policy, its iterates converge in Wasserstein distance to the optimal joint return law.
This work describes the EGGROLL update mean field at finite rank and nonzero perturbation radius as a resolvent applied to the gradient of the perturbation-smoothed objective, and introduces LOO-ROLL, a leave-one-out estimator that replaces EGGROLL's two antithetic evaluations per direction by one.
Finite-iteration behavior of the exact asynchronous recursions used by categorical distributional temporal-difference methods is studied and analogous finite-iteration guarantees for horizon-stacked categorical methods under episodic sampling are established.
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