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Diffusion-MPC in Discrete Domains: Feasibility Constraints, Horizon Effects, and Critic Alignment: Case study with Tetris

arXiv – CS AI|Haochuan Kevin Wang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers studied diffusion-based model predictive control in discrete domains using Tetris, finding that feasibility constraints are necessary and shorter planning horizons outperform longer ones. The study reveals structural challenges with discrete diffusion planners, particularly misalignment issues with DQN critics that produce high decision regret.

Key Takeaways

→Feasibility masking is essential in discrete domains, removing 46% of invalid actions and improving performance by 6.8% in score and 5.6% in survival.
→DQN critic reranking shows systematic misalignment with rollout quality, producing mean decision regret of 17.6.
→Shorter planning horizons outperform longer ones under sparse rewards due to uncertainty compounding in extended rollouts.
→Compute allocation between candidate count and horizon length determines failure modes in diffusion planners.
→The research provides practical diagnostics for integrating critics with diffusion-based planning systems.

#diffusion-models #model-predictive-control #discrete-domains #reinforcement-learning #planning-algorithms #tetris #dqn #feasibility-constraints #critic-alignment #arxiv

Read Original →via arXiv – CS AI

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