Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction

2026-08-07

AI Summary

This work addresses the limitations of traditional world models that rely on recursive multi-step prediction, which often suffer from error accumulation and degraded long-horizon forecasting accuracy. The authors propose a novel end-to-end, non-recursive Direct Prediction World Model (DPWM) that compresses action sequences of arbitrary length into a single embedding and directly predicts the final state in a single forward pass. By avoiding recursive unrolling, DPWM mitigates error propagation and training instability while enabling gradient flow over extended horizons. Evaluated on continuous control and pixel-based benchmarks, the method consistently outperforms existing recursive models, with performance gains becoming more pronounced as the prediction horizon increases—demonstrating the critical role of terminal-state prediction as an effective objective for long-horizon tasks.

Abstract

World models are expected to support imagination over extended temporal horizons, yet most are still trained through local few-step prediction objectives and deployed by recursively rolling out their own predictions. This creates a fundamental mismatch: few-step losses optimize local transition fidelity, while long-horizon prediction depends on how errors and gradients propagate through the entire trajectory. As a result, transitions with different downstream influence on the endpoint are treated uniformly during training, and small local errors are amplified through recursive inference. We argue that long-horizon accuracy is better achieved by optimizing directly, through an end-to-end endpoint prediction objective. To instantiate this paradigm, we introduce the Direct Prediction World Model (DPWM), a non-recursive architecture that compresses an action sequence of arbitrary length into a single embedding and predicts the endpoint observation in a single forward pass. This design avoids recurrent rollout in both prediction and gradient propagation, making long-horizon end-to-end training practical at horizons where unrolled autoregressive training becomes unstable. Empirically, DPWM substantially improves long-horizon endpoint prediction over recursive world-model baselines on continuous-control and pixel-based benchmarks, with larger gains as the prediction horizon increases. We further show that recurrent baselines benefit similarly when retrained with the same long-horizon endpoint objective, supporting our central claim that the training objective, rather than the particular backbone choice, is the main driver of long-horizon prediction accuracy. Our results suggest that world models can benefit from being trained and evaluated at the temporal scales where they are ultimately used, shifting the focus from local transition modeling toward long-horizon predictive accuracy.

Problem

• world models
• long-horizon prediction
• end-to-end training
• recursive rollout
• prediction accuracy

Innovation

• long-horizon prediction
• end-to-end training
• world models
• non-recursive architecture
• direct future prediction