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Gokcen Eraslan

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2 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-based Decoding

Xiner Li
Yulai Zhao
Chenyu Wang
Gabriele Scalia
Gokcen Eraslan
Surag Nair
Tommaso Biancalani
Shuiwang Ji

Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. However, rather than merely generating designs that are natural, we often aim to optimize downstream reward functions while preserving the naturalness of these design spaces. Existing methods for achieving this goal often require differentiable proxy models (e. g. , classifier guidance or DPS) or involve computationally expensive fine-tuning of diffusion models (e. g. , classifier-free guidance, RL-based fine-tuning). In our work, we propose a new method to address these challenges. Our algorithm is an iterative sampling method that integrates soft value functions, which looks ahead to how intermediate noisy states lead to high rewards in the future, into the standard inference procedure of pre-trained diffusion models. Notably, our approach avoids fine-tuning generative models and eliminates the need to construct differentiable models. This enables us to (1) directly utilize non-differentiable features/reward feedback, commonly used in many scientific domains, and (2) apply our method to recent discrete diffusion models in a principled way. Finally, we demonstrate the effectiveness of our algorithm across several domains, including image generation, molecule generation, and DNA/RNA sequence generation.

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NeurIPS Conference 2024 Conference Paper

Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models

Masatoshi Uehara
Yulai Zhao
Ehsan Hajiramezanali
Gabriele Scalia
Gokcen Eraslan
Avantika Lal
Sergey Levine
Tommaso Biancalani

AI-driven design problems, such as DNA/protein sequence design, are commonly tackled from two angles: generative modeling, which efficiently captures the feasible design space (e. g. , natural images or biological sequences), and model-based optimization, which utilizes reward models for extrapolation. To combine the strengths of both approaches, we adopt a hybrid method that fine-tunes cutting-edge diffusion models by optimizing reward models through RL. Although prior work has explored similar avenues, they primarily focus on scenarios where accurate reward models are accessible. In contrast, we concentrate on an offline setting where a reward model is unknown, and we must learn from static offline datasets, a common scenario in scientific domains. In offline scenarios, existing approaches tend to suffer from overoptimization, as they may be misled by the reward model in out-of-distribution regions. To address this, we introduce a conservative fine-tuning approach, BRAID, by optimizing a conservative reward model, which includes additional penalization outside of offline data distributions. Through empirical and theoretical analysis, we demonstrate the capability of our approach to outperform the best designs in offline data, leveraging the extrapolation capabilities of reward models while avoiding the generation of invalid designs through pre-trained diffusion models.

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