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GRAS Improves Training-Free Reward Alignment in Diffusion Models.

Kwanyoung Kim· August 28, 2026 View original

Key takeaways

  • GRAS enhances training-free reward alignment in discrete diffusion models.
  • It reduces gradient estimator variance and uses adaptive particle resampling.
  • GRAS achieves state-of-the-art reward performance without model retraining.
  • It is effective for both differentiable and non-differentiable rewards.

Who benefits

PharmaceuticalsBiotechnologyMaterials ScienceAI/ML DevelopmentCreative Industries

Summary

GRAS (Guided Reduced-variance proposals and Adaptive Selection) enhances training-free reward alignment in discrete diffusion models by reducing gradient estimator variance and adaptively resampling particles. This method achieves state-of-the-art reward performance in tasks like regulatory DNA and protein design without retraining the model.

Discrete diffusion models are powerful generators for sequence data, but steering them towards specific downstream rewards during inference without retraining remains a challenge. Existing training-free steering methods, which combine gradient guidance and search, often suffer from noisy gradient estimates and suboptimal particle resampling strategies. This paper introduces GRAS (Guided Reduced-variance proposals and Adaptive Selection) to address these weaknesses. GRAS implements two key improvements without adding denoiser cost. First, it reduces the variance of the gradient estimator through Rao-Blackwellized reveal for differentiable rewards and a leave-one-out baseline for non-differentiable ones. Second, it introduces an adaptive resampling temperature for the search process, standardizing per-step values into a group-relative advantage. These changes enable GRAS to achieve superior training-free reward alignment across diverse applications like regulatory DNA and protein design, matching or even surpassing the performance of reward-fine-tuned models, even with non-differentiable rewards.

Why it matters

For professionals working with generative AI, particularly in fields like drug discovery, material science, or creative content generation, GRAS offers a highly efficient way to guide diffusion models towards desired outcomes without the significant computational cost and time of retraining. This accelerates design cycles and improves the utility of generative models.

How to implement this in your domain

  1. 1Integrate GRAS into existing discrete diffusion model pipelines for training-free reward alignment.
  2. 2Experiment with GRAS for guiding generative models in tasks like molecular design or sequence optimization.
  3. 3Apply the reduced-variance proposal techniques to improve gradient estimation in other generative models.
  4. 4Develop adaptive resampling strategies based on GRAS principles for various search-based optimization problems.
  5. 5Evaluate the performance and efficiency gains of GRAS compared to fine-tuning or other training-free methods.

Original post by Kwanyoung Kim

"arXiv:2608.26585v1 Announce Type: new Abstract: Discrete diffusion models have become a strong, widely adopted class of generators for sequence data, and steering them toward a downstream reward at inference time, without any retraining, is increasingly important. Such training-f…"

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