# Horizon-Scalable Q-Learning

**Authors:** Haoran Xu, Pragya Srivastava, Xinjie Liu, Siddhant Agarwal, Amy Zhang  
**Lab:** The University of Texas at Austin · **Published:** 6 Nov 2026 · **Upvotes:** 1  
**Links:** [arXiv](https://arxiv.org/abs/2611.10987) · [PDF](https://arxiv.org/pdf/2611.10987) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.10987.html)

## Abstract

Learning accurate off-policy state-action value functions (Q-functions) from offline datasets becomes harder as the task horizon grows. One-step temporal-difference (TD) learning bootstraps after every transition, so small per-step errors accumulate along the horizon, and early states can be badly misvalued even when the TD loss is small. Multi-step TD shortens this chain by following the dataset trajectory for several steps; however, since the behavior policy is often suboptimal, every followed action adds a bias, and no fixed number of steps suits a whole trajectory. In this paper, we propose Horizon-Scalable Q-Learning (HQL), a simple method that enables accurate long-horizon value learning by automatically balancing between one-step and multi-step returns. HQL extends a target through a dataset action only when the critic values it at least as highly as the best-of-N actions sampled from the BC policy, and otherwise falls back to a one-step target. We show that this rule is a principled off-policy correction that needs no action densities, which makes HQL compatible with expressive flow and diffusion policies. Experiments show that HQL avoids the failures of both one-step and multi-step TD on a combination-lock task across horizons, and outperforms prior horizon-reduction methods on long-horizon OGBench and OCBench tasks.

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