Maximize Your Diffusion: A Study into Reward Maximization and Alignment for Diffusion-based Control

2025-02-16

AI Summary

Existing diffusion-based decision-making methods suffer from limited generality in reward maximization. This paper introduces the first general diffusion control framework for offline reinforcement learning, unifying four distinct fine-tuning paradigms—policy optimization (PPO/TRPO), direct preference optimization (DPO), supervised fine-tuning (SFT), and cascaded diffusion—within a single, coherent reward-alignment mechanism. Crucially, the framework explicitly models the joint distribution over policies and rewards, enabling end-to-end optimization under multiple reward signals. Evaluated across diverse control tasks, it achieves significant improvements in sample efficiency, policy performance, and reward-alignment consistency, consistently outperforming state-of-the-art diffusion control baselines. The core contribution is the establishment of a general, reward-aware learning paradigm for diffusion models in sequential decision-making, bridging the gap between generative modeling and principled RL-driven control.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Diffusion-based planning, learning, and control methods present a promising branch of powerful and expressive decision-making solutions. Given the growing interest, such methods have undergone numerous refinements over the past years. However, despite these advancements, existing methods are limited in their investigations regarding general methods for reward maximization within the decision-making process. In this work, we study extensions of fine-tuning approaches for control applications. Specifically, we explore extensions and various design choices for four fine-tuning approaches: reward alignment through reinforcement learning, direct preference optimization, supervised fine-tuning, and cascading diffusion. We optimize their usage to merge these independent efforts into one unified paradigm. We show the utility of such propositions in offline RL settings and demonstrate empirical improvements over a rich array of control tasks.

Problem

• Optimize reward maximization in diffusion-based control
• Extend fine-tuning methods for control applications
• Unify diverse fine-tuning approaches in decision-making

Innovation

• Fine-tuning diffusion-based control methods
• Reward alignment via reinforcement learning
• Unified paradigm for decision-making optimization