Horizon Reduction as Information Loss in Offline Reinforcement Learning

TL;DR

This paper demonstrates that horizon reduction in offline reinforcement learning can cause fundamental information loss, leading to potential policy indistinguishability and structural failure modes, which limits its theoretical safety and effectiveness.

Contribution

The paper formalizes horizon reduction as learning from fixed-length segments and proves its inherent information loss, identifying three key failure modes through minimal counterexample MDPs.

Findings

Horizon reduction can cause irrecoverable information loss in offline RL.

Optimal policies may be indistinguishable from suboptimal ones under horizon truncation.

Identifies three structural failure modes: prefix indistinguishability, objective misspecification, and dataset support aliasing.

Abstract

Horizon reduction is a common design strategy in offline reinforcement learning (RL), used to mitigate long-horizon credit assignment, improve stability, and enable scalable learning through truncated rollouts, windowed training, or hierarchical decomposition (Levine et al., 2020; Prudencio et al., 2023; Park et al., 2025). Despite recent empirical evidence that horizon reduction can improve scaling on challenging offline RL benchmarks, its theoretical implications remain underdeveloped (Park et al., 2025). In this paper, we show that horizon reduction can induce fundamental and irrecoverable information loss in offline RL. We formalize horizon reduction as learning from fixed-length trajectory segments and prove that, under this paradigm and any learning interface restricted to fixed-length trajectory segments, optimal policies may be statistically indistinguishable from suboptimal ones even with infinite data and perfect function approximation. Through a set of minimal counterexample Markov decision processes (MDPs), we identify three distinct structural failure modes: (i) prefix indistinguishability leading to identifiability failure, (ii) objective misspecification induced by truncated returns, and (iii) offline dataset support and representation aliasing. Our results establish necessary conditions under which horizon reduction can be safe and highlight intrinsic limitations that cannot be overcome by algorithmic improvements alone, complementing algorithmic work on conservative objectives and distribution shift that addresses a different axis of offline RL difficulty (Fujimoto et al., 2019; Kumar et al., 2020; Gulcehre et al., 2020).