Improving Long Horizon Planning via Better Objectives

Summary & Methodology Analysis

The researchers investigated why latent world models, which are systems that learn a compact internal representation of environment dynamics to simulate future outcomes, often struggle with long horizon planning. The team found that the bottleneck is not the model capacity or the prediction horizon itself, but the specific objective function used during the planning phase. By analyzing the latent space geometry through pairwise squared Euclidean distances and validating that positional information exists within the embeddings via a linear ridge probe (a simple linear model used to evaluate if features are linearly separable), they proved the necessary data for better planning was already present and ignored by the standard objective. The authors developed new metrics, including a decoded position distance and a temporal distance cost function (a learned model that estimates the number of frames between two environment states), to score potential action trajectories in the Cross Entropy Method (CEM) planner, an iterative search algorithm for optimizing sequences of actions. When these replacement objectives were used instead of the original squared latent distance, planning performance increased substantially, moving success at a 100 frame offset from 14.0 percent to 70.0 percent on released model weights and up to 98.0 percent on reproduction checkpoints. The study is limited to the TwoRoom diagnostic environment. Furthermore, the analysis uses only one seed per checkpoint, which leaves uncertainty regarding seed variance, and the study cannot isolate specific causes of metric degradation caused by confounding variables like history size or action width.