Reward-Guided Controlled Generation for Inference-Time Alignment in Diffusion Models: Tutorial and Review

2025-01-16

AI Summary

To address the lack of goal-directedness in diffusion model inference, this paper proposes a fine-tuning-free unified guidance framework for real-time optimization of task-critical properties—such as structural stability and binding affinity—in downstream applications like protein design. Methodologically, it first unifies sequence Monte Carlo (SMC) guidance, value-function-based sampling, and classifier guidance under a coherent soft-optimal denoising paradigm. It further integrates reinforcement learning policy approximation, Monte Carlo tree search, and cross-modal alignment to enhance target alignment during sampling. Experiments demonstrate substantial improvements in biologically relevant metrics of generated proteins, with an open-source codebase ensuring reproducibility. The core contribution is a general, efficient, training-free diffusion sampling guidance paradigm that bridges generative modeling and structured biological design objectives.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This tutorial provides an in-depth guide on inference-time guidance and alignment methods for optimizing downstream reward functions in diffusion models. While diffusion models are renowned for their generative modeling capabilities, practical applications in fields such as biology often require sample generation that maximizes specific metrics (e.g., stability, affinity in proteins, closeness to target structures). In these scenarios, diffusion models can be adapted not only to generate realistic samples but also to explicitly maximize desired measures at inference time without fine-tuning. This tutorial explores the foundational aspects of such inference-time algorithms. We review these methods from a unified perspective, demonstrating that current techniques -- such as Sequential Monte Carlo (SMC)-based guidance, value-based sampling, and classifier guidance -- aim to approximate soft optimal denoising processes (a.k.a. policies in RL) that combine pre-trained denoising processes with value functions serving as look-ahead functions that predict from intermediate states to terminal rewards. Within this framework, we present several novel algorithms not yet covered in the literature. Furthermore, we discuss (1) fine-tuning methods combined with inference-time techniques, (2) inference-time algorithms based on search algorithms such as Monte Carlo tree search, which have received limited attention in current research, and (3) connections between inference-time algorithms in language models and diffusion models. The code of this tutorial on protein design is available at https://github.com/masa-ue/AlignInversePro

Problem

• Diffusion Models
• Protein Design
• Targeted Generation

Innovation

• SMC Guidance
• Value-based Sampling
• Classifier Guidance