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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
The Objective Is the Bottleneck: Latent World Models Encode What Their Planners Cannot Use
TL;DR
Latent world models can predict future states effectively, but their planning performance is limited by the objective function used. By changing the objective, planning success significantly improves, demonstrating that the planner's goal is the real bottleneck.
✦ Why It Matters
Evaluate and adjust your planning objectives to enhance long-horizon success in AI models today.
Key Takeaways
- Assess the objective function in your planning models.
- Consider reachability over proximity for better planning outcomes.
- Experiment with objective replacements to improve performance.
- Monitor the correlation between prediction accuracy and planning success.
How It Works
The study demonstrates that the latent world model's predictor can accurately imagine states far into the future, but the planner's effectiveness is constrained by its objective function. By changing the objective from minimizing squared latent distance to a more suitable one, the planning horizon can be extended without retraining the model, leading to significantly improved goal achievement rates.