Plug-and-Play Guidance for Discrete Diffusion Models via Gradient-Informed Logit Correction

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Life Sciences & Biology, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

Gradient-Informed Logit Correction (GILC) is a novel plug-and-play framework designed to enable controllable generation with discrete diffusion models, addressing the common issues of high computational overhead or the need for retraining. GILC efficiently estimates guidance signals by repurposing the pretrained denoising network as a variational proxy. It introduces a Jacobian-free mechanism that directly corrects clean prediction logits, ensuring stable and effective guidance even in high-dimensional discrete spaces. The method is versatile, accommodating both differentiable and non-differentiable reward functions. Extensive experiments across DNA, protein sequence, and molecular generation tasks demonstrate that GILC achieves state-of-the-art performance without additional training, frequently outperforming traditional fine-tuning approaches.

Key takeaway

For Machine Learning Engineers or Research Scientists needing controllable generation from discrete diffusion models without extensive retraining, GILC offers a highly efficient, training-free solution. You should consider implementing GILC-DB for tasks with differentiable reward functions, or GILC-PG when dealing with non-differentiable or black-box objectives, to achieve state-of-the-art results in domains like DNA, protein, or molecule design.

Key insights

GILC guides discrete diffusion by correcting clean prediction logits using a variational proxy and Jacobian-free gradient estimation.

Principles

• Pretrained denoising networks can serve as variational proxies for value function estimation.
• Jacobian-free logit correction stabilizes gradient-based guidance in discrete spaces.
• Policy gradients enable guidance with non-differentiable reward functions.

Method

GILC estimates value function gradients via a variational proxy from the denoising network, then applies a Jacobian-free correction to clean prediction logits using Gumbel-Softmax and Straight-Through estimators.

In practice

• Use GILC-DB for differentiable rewards to minimize reward evaluations.
• Employ GILC-PG for black-box or non-differentiable objectives.
• Prioritize reward function calls in early sampling stages for better accuracy.

Topics

• Discrete Diffusion Models
• Controllable Generation
• Gradient-Informed Logit Correction
• Protein Sequence Design
• DNA Sequence Design
• Molecular Generation

Code references

• JD-P/simulacra-aesthetic-captions

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.