Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.24461

Measuring Empowerment from Offline Data for Intrinsically Motivated Exploration

Ishir Garg·Vivek Myers·Qiyang Li·Catherine Ji·Benjamin Eysenbach·Sergey Levine
View original source ↗Read source document ↗Export metadata ↓

Abstract

Large, unlabeled datasets of prior experience have become increasingly available for agentic tasks in domains such as robotics and coding. A common recipe for these tasks is to train a policy using the prior data, and then further improve it with online rollouts. But while much work has studied how to use both the offline and online data for policy extraction, far less work has been done to understand how the prior data can be used to efficiently \textit{explore} during the online learning phase. Previous work has attempted to formalize this idea by defining the \textit{empowerment}, a measure of how many future states an agent can realize; states with higher empowerment require more exploration as there are more futures to explore. However, existing methods for estimating empowerment require online policy rollouts in the environment. We propose the first method for estimating empowerment from unlabeled prior data, without any environment interaction. By jointly learning an empowerment-maximizing policy together with its discounted state-occupancy measure, our estimator can be trained on unlabeled trajectories generated from any unknown policy. We demonstrate that our empowerment estimator qualitatively gives intuitive empowerment measures and show that the pretrained estimator can be used to improve exploration in the online goal-conditioned reinforcement learning setting. Code is available at \url{https://github.com/ishirgarg/offline-empowerment}.

Explore related subjects

cs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ishir Garg, Vivek Myers, Qiyang Li, Catherine Ji, Benjamin Eysenbach, Sergey Levine. 2026-10-17. Measuring Empowerment from Offline Data for Intrinsically Motivated Exploration. https://arxiv.org/abs/2610.24461

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Diffusion models (DMs) have proven to be effective in modeling high-dimensional distributions, leading to their widespread adoption for representing complex priors in Bayesian inverse problems (BIPs). However, current DM-based posterior sampling methods proposed for solving common BIPs rely on heuristic approximations to the generative process. To exploit the generative capability of DMs and avoid the usage of such approximations, we propose an ensemble-based algorithm that performs posterior sampling without the use of heuristic approximations. Our algorithm is motivated by existing work that combines DM-based methods with the sequential Monte Carlo (SMC) method. By examining how the prior evolves through the diffusion process encoded by the pre-trained score function, we derive a modified partial differential equation (PDE) governing the evolution of the corresponding posterior distribution. This PDE includes a modified diffusion term and a reweighting term, which can be simulated via stochastic weighted particle methods. Theoretically, we prove that the error between the true posterior and the empirical distribution of the generated samples can be bounded in terms of the training error of the pre-trained score function and the number of particles in the ensemble. Empirically, we validate our algorithm on several inverse problems in imaging to show that our method gives more accurate reconstructions compared to existing DM-based methods. Our code is available at the following Github repository~\url{https://github.com/HaoxuanSteveC00/AFDPS-TMLR}.

cs.LG↗
arXiv2026

Optimal hypersurface decision trees

Optimal decision trees have attracted increasing attention in recent years, yet existing algorithms face two closely related challenges. First, the trees they construct have limited expressivity because their splitting rules are typically restricted to axis-parallel splits or binary features. Second, these algorithms generally scale poorly to large datasets. These challenges compound one another: more expressive splitting rules increase the combinatorial complexity of the search space, making the resulting optimal decision tree (ODT) problem even more difficult to solve. Building on the proper decision tree framework of \citet{he2025odt}, we propose, to the best of our knowledge, the first algorithm for constructing optimal decision trees with hypersurface splits. The algorithm has worst-case work complexity $O\left(K!K N^{KG+1}\right)$ when $N\geq K$ and fixed-dimensional feasibility tests are treated as constant-cost operations, where $G=\binom{D+M}{M}-1$ depends on the hypersurface polynomial degree $M$ and the data dimension $D$. Its structure is naturally amenable to vectorization and parallelization, enabling efficient execution on GPUs, and the generic algorithm design can be used to accelerate other ODT variants, such as axis-parallel decision (ADT) trees. We further develop a safe pruning strategy that substantially reduces the number of candidate configurations and an incremental generation procedure that reduces the associated feasibility-checking cost from $O(K^2)$ to $O(K)$.

cs.LG↗
arXiv2026

SPRINT: Stochastic Performative Prediction With Variance Reduction

Performative prediction (PP) is an algorithmic framework for optimizing machine learning (ML) models where the model's deployment affects the distribution of the data it is trained on. Compared to traditional ML with fixed data, designing algorithms in PP converging to a stable point -- known as a stationary performative stable (SPS) solution -- is more challenging than the counterpart in conventional ML tasks due to the model-induced distribution shifts. While considerable efforts have been made to find SPS solutions using methods such as repeated gradient descent (RGD) and greedy stochastic gradient descent (SGD-GD), most prior studies assumed a strongly convex loss until a recent work established $O(1/\sqrt{T})$ convergence of SGD-GD to SPS solutions under smooth, non-convex losses. However, this latest progress is still based on the restricted bounded variance assumption in stochastic gradient estimates and yields convergence bounds with a non-vanishing error neighborhood that scales with the variance. This limitation motivates us to improve convergence rates and reduce error in stochastic optimization for PP, particularly in non-convex settings. Thus, we propose a new algorithm called stochastic performative prediction with variance reduction (SPRINT) and establish its convergence to an SPS solution at a rate of $O(1/T)$. Notably, the resulting error neighborhood is independent of the variance of the stochastic gradients. Experiments on multiple real datasets with non-convex models demonstrate that SPRINT outperforms SGD-GD in both convergence rate and stability.

cs.LG↗