## Horizon Reduction and Data Loss in Reinforcement Learning A recent study published on arXiv examines the impact of Horizon Reduction (HR) in offline Reinforcement Learning (RL). Horizon Reduction is a common design strategy used to mitigate long-horizon credit assignment, improve stability, and enable scalable learning through truncated rollouts, windowed training, or hierarchical decomposition. The research highlights how HR can induce a fundamental and irrecoverable loss of information. The researchers formalized HR as learning from fixed-length trajectory segments, demonstrating that, in this paradigm, optimal policies may be statistically indistinguishable from suboptimal ones, even with an infinite amount of data. ## Three Structural Failure Modes The study identifies three distinct structural failure modes: 1. **Prefix indistinguishability:** leading to identifiability failure. 2. **Objective misspecification:** induced by truncated returns. 3. **Offline dataset support and representation aliasing.** The results establish the necessary conditions for Horizon Reduction to be safe, highlighting intrinsic limitations that cannot be overcome by algorithmic improvements alone. This work complements studies on conservative objectives and distribution shift, which address a different axis of offline RL difficulty.