arXiv:2604. 10727v2 Announce Type: replace-cross Abstract: Classical information-theoretic learning bounds typically rely on KL mutual information and moment-generating-function (MGF) arguments, which are well matched to bounded or sub-Gaussian losses but can be ineffective when losses or rewards are heavy-tailed.
By Huiming Zhang, Binghan Li, Wan Tian, Qiang Sun
The paper introduces the concept of behavior-consistent deep reinforcement learning, aiming to produce high-performing policies that remain distributionally similar across different training runs. It shows that maximum-entropy RL can control behavioral divergence by anchoring runs to a common prior, and proves that for Boltzmann policies, a temperature proportional to Q‑function disagreement limits pairwise KL divergence. Building on this, the authors propose Q‑value Expectile Disagreement (QED), a state‑dependent temperature schedule that uses double‑critic disagreement to approximate cross‑run disagreement, and demonstrate that QED reduces across‑run divergence by two orders of magnitude on 18 continuous‑control tasks without sacrificing performance.
By Marcel Hussing, Liv G. d'Aliberti, Claas Voelcker, Benjamin Eysenbach, Eric Eaton
arXiv:2605. 28983v2 Announce Type: replace-cross Abstract: In this paper, training a neural network is identified, exactly, as a search through Hamilton--Jacobi initial-value problems: each gradient step selects the initial data of a viscous Hamilton--Jacobi equation whose Hopf--Cole propagator best fits the observations; at inference, the input is the spatial point at which that solution is evaluated and the initial condition is already encoded in the weights.
By Jose Marie Antonio Mi\~noza, Erika Fille T. Legara, Christopher P. Monterola
arXiv:2607. 17316v1 Announce Type: new Abstract: The softmax policy $\pi(a \mid s) \propto \exp(\beta Q(s,a))$ is the default model of stochastic choice in reinforcement learning (RL).
By Silviu Pitis
arXiv:2601. 08136v2 Announce Type: replace Abstract: Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge.
By Zeyang Li, Sunbochen Tang, Navid Azizan
The paper proposes using the $q$Gaussian distribution, derived from Tsallis entropy maximization, to address the shortcomings of Gaussian assumptions in sparse learning with correlated and heterogeneous data. It introduces a new framework that adapts numerical equilibrium methods to composite optimization problems, applying it to the Hager‑Zhang conjugate gradient algorithm to create a stable, efficient sparse learning algorithm. The work offers both theoretical insights into alternative statistical distributions and practical tools for data analysis in fields like biostatistics.
By Kai Yang, Masoud Asgharian, Celia M. T. Greenwood