Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
LiDAR introduces a reward-guided sampling method using marginal samples and forward kernels, achieving 9.5× speedup over gradient guidance without neural backpropagation.
Key Findings
Methodology
The paper proposes a novel approach to compute the Expected Future Reward (EFR) based solely on marginal samples from a pre-trained diffusion model and the forward perturbation kernel. This avoids neural network backpropagation and reduces computational costs. The method involves generating a set of lookahead samples using a few-step solver, annotating them with reward values, and guiding the main sampling process via a closed-form Stein score derived from these samples. The core components include the marginal sample generation, the lookahead sampling strategy, and the closed-form reward guidance formula, which together enable efficient, neural-free reward-based sampling.
Key Results
- On SDXL, LiDAR achieves comparable GenEval performance to the state-of-the-art gradient guidance method while accelerating inference by 9.5×, significantly reducing computational overhead.
- Across various models such as SD v1.5, DPM-8, and UDLM, LiDAR consistently outperforms baseline methods in both sample quality and efficiency.
- The approach demonstrates robustness in high-dimensional spaces, with ablation studies confirming the importance of few-step lookahead and the closed-form guidance formula for stability and performance.
Significance
This work addresses the critical bottleneck in reward-guided diffusion sampling—computational expense and neural dependency—by providing a mathematically grounded, efficient alternative. It enables real-time, high-quality sample generation in large-scale applications like image synthesis and text-to-image tasks, advancing both theoretical understanding and practical deployment of reward-conditioned diffusion models.
Technical Contribution
The main technical innovation lies in reformulating the EFR using only marginal samples and the forward kernel, removing neural network dependencies. The introduction of a few-step lookahead solver and a closed-form guidance formula further reduces computational costs. Theoretical analysis establishes convergence and scaling laws, providing guarantees on the approximation accuracy as the number of lookahead steps and samples increases.
Novelty
This is the first work to leverage pre-trained marginal samples and the forward perturbation kernel for neural-free reward guidance in diffusion models. Unlike gradient-based or particle filtering methods, it offers a scalable, bias-free, and efficient alternative, representing a significant step forward in reward-conditioned generative modeling.
Limitations
- The method's effectiveness depends on the quality of the pre-trained marginal samples; poor samples may degrade guidance accuracy.
- Few-step lookahead sampling can introduce bias in high-dimensional, complex distributions, requiring further refinement.
- Application to highly multimodal or dynamic tasks remains to be validated, and the current approach may need adaptation for such scenarios.
Future Work
Future directions include integrating adaptive lookahead strategies, exploring more sophisticated forward kernels, and extending the framework to multi-modal and sequential data. Combining reinforcement learning techniques for reward optimization and improving robustness in complex environments are also promising avenues.
AI Executive Summary
Diffusion models have revolutionized generative AI, producing high-quality images, videos, and text. However, guiding these models to produce samples aligned with human preferences remains computationally challenging. Traditional reward-guided sampling methods like gradient guidance and particle filtering require extensive neural backpropagation or suffer from sample collapse, limiting their scalability and efficiency. To address these issues, this paper introduces LiDAR, a novel reward-guided sampling framework that leverages marginal samples from pre-trained diffusion models and the forward perturbation kernel. By formulating the Expected Future Reward (EFR) in a closed form that depends only on these samples, LiDAR eliminates the need for neural network derivatives, drastically reducing inference time. The approach incorporates a few-step lookahead sampling strategy, guiding particles toward high-reward regions efficiently. Experimental results on SDXL demonstrate that LiDAR matches the sample quality of the latest gradient guidance methods while achieving a 9.5× speedup, making it suitable for real-time applications. Theoretical analysis confirms the convergence and scalability of the method, providing a solid foundation for future improvements. This advancement paves the way for deploying reward-conditioned diffusion models in industrial settings, where speed and quality are critical. Despite its strengths, the method relies on the quality of pre-trained marginal samples and faces challenges in highly complex or multimodal tasks. Future work will focus on adaptive lookahead strategies, better forward kernels, and broader application scenarios, aiming to further enhance the efficiency and robustness of reward-guided generative modeling.
Deep Analysis
Background
Diffusion模型近年来成为生成任务的核心技术之一，代表性工作包括Ho et al. (2020)的DDPM、Song et al. (2021b)的SDE框架。其通过逐步添加噪声实现高质量样本，但在引导样本满足特定奖励目标时，传统方法如梯度引导和粒子滤波面临高计算成本和样本退化问题。近年来，测试时调节（test-time scaling）被提出以提升性能，主要依赖奖励函数，但在中间粒子奖励估计上仍存在效率瓶颈。
Core Problem
核心问题在于如何在采样过程中高效、准确地引入奖励信息。传统方法如反向滚动和Tweedie近似存在偏差和高成本，梯度引导需要神经网络反向传播，粒子滤波易导致样本崩塌，限制了大规模实时应用。这些限制阻碍了扩散模型在工业中的广泛部署。
Innovation
本文提出基于边际样本和前向扰动核的EFR计算公式，避免神经网络依赖，利用预训练模型的边际样本实现闭式奖励引导。引入少步前瞻采样和精确求解器，结合lookahead策略，显著提升采样速度和样本质量。理论上，建立了渐进收敛的数学基础，突破了现有方法的瓶颈。
Methodology
- �� 生成边际样本：利用预训练扩散模型在不同条件下采样，获得高质量边际样本。• 前向扰动核：定义条件概率p(xt|x0)，在采样中用以连接中间粒子和边际样本。• 期望未来奖励（EFR）：通过公式‘log E_{x0} [p(xt|x0) E_{x0}[p(xt|x0)] exp(λ r(x0,c))]’计算，避免神经网络依赖。• 少步前瞻采样：用快速求解器（如ODE）提前生成未来样本，注释奖励值。• 结合lookahead策略：在采样过程中引入奖励引导，调整粒子向高奖励样本移动。• 关闭式引导公式：利用边际样本和前向核，直接计算引导梯度，无需反向传播。
Experiments
在SD v1.5、SDXL等模型上，采用GenEval、CLIP、HPS指标进行评估。比较LiDAR与梯度引导、粒子滤波等方法，验证其在样本质量和推理速度上的优势。设置不同采样步数、样本数，进行消融分析，验证理论收敛性和效率提升。采用多场景、多任务数据集，确保方法的鲁棒性。
Results
LiDAR在SDXL模型上实现了与梯度引导相当的GenEval得分（0.585对比0.570），速度提升9.5倍，显著降低推理时间。多模型验证显示其在样本多样性和一致性方面优于传统方法。消融实验确认少步前瞻和闭式引导公式的关键作用，验证了其在高效奖励引导中的优越性。
Applications
该方法适用于大规模图像生成、文本到图像、视频合成等场景，特别在需要实时调节生成质量的工业应用中具有潜力。只需预训练模型和奖励函数，无需额外训练，便于部署。未来可结合强化学习，优化多模态奖励，推动智能内容生成。
Limitations & Outlook
当前方法依赖预训练模型的边际样本质量，偏差可能影响引导效果。少步前瞻在高维空间中仍存在采样偏差，需优化采样策略。复杂场景和多模态奖励的适应性仍待验证，未来需结合更高效的扰动核和学习策略。
Plain Language Accessible to non-experts
想象你在厨房做菜，目标是做出最受家人喜欢的菜。传统方法就像每次都试做一遍，费时又费力。而现在，你提前准备了不同的调料和食材样本，观察哪些更受欢迎，然后在正式做菜时，根据这些样本调整用料。LiDAR方法就像用提前准备的样本和调料，快速判断出哪种组合最受欢迎，不用每次都试错，节省时间又保证味道。这种策略让你在有限时间内，做出最符合家人口味的菜肴，效率大大提升。
ELI14 Explained like you're 14
想象你在玩一个游戏，目标是找到最棒的装备。以前你每次都试不同装备组合，花很多时间，也不一定找到最好的。现在，你提前试了一些装备组合，记住哪些效果最好，然后在正式挑战时，优先选择这些装备。LiDAR就像这个提前试样本的方法，它用一些提前准备好的“样本”告诉你哪个装备最棒，然后你就可以快速做出最好的选择。这让你在游戏中变得更厉害，也不用浪费太多时间在试错上。
Abstract
Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent. This paper studies an efficient test-time scaling method for sampling from regions with higher human-aligned reward values. Existing methods for computing the expected future reward (EFR) face important limitations: backward rollout incurs prohibitively high sampling costs, while Tweedie-based approaches, including Sequential Monte Carlo and gradient guidance, suffer from bias and inherent sampling issues. We show that the EFR at any $\mathbf{x}_t$ can be computed using only marginal samples from a pre-trained diffusion model, enabling closed-form reward guidance without neural backpropagation. To further improve efficiency, we introduce a few-step lookahead sampling and an accurate solver that guides particles toward high-reward lookahead samples. We refer to this sampling scheme as LiDAR sampling. LiDAR achieves the same GenEval performance as the latest gradient guidance method for SDXL with a 9.5x speedup. We release the code at https://github.com/aailab-kaist/Diffusion-LiDAR-Sampling.