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Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design
arXiv – CS AI|Xingyu Su, Xiner Li, Masatoshi Uehara, Sunwoo Kim, Yulai Zhao, Gabriele Scalia, Ehsan Hajiramezanali, Tommaso Biancalani, Degui Zhi, Shuiwang Ji||4 views
🤖AI Summary
Researchers propose a new iterative distillation framework for fine-tuning diffusion models in biomolecular design that optimizes for specific reward functions. The method addresses stability and efficiency issues in existing reinforcement learning approaches by using off-policy data collection and KL divergence minimization for improved training stability.
Key Takeaways
- →New iterative distillation framework enables diffusion models to optimize for arbitrary reward functions in biomolecular design.
- →Method addresses instability and low sample efficiency issues common in reinforcement learning approaches for diffusion model fine-tuning.
- →Off-policy formulation combined with KL divergence minimization enhances training stability compared to existing RL-based methods.
- →Framework demonstrates effectiveness across protein, small molecule, and regulatory DNA design tasks.
- →Source code has been made publicly available for research community adoption.
#diffusion-models#biomolecular-design#machine-learning#reward-optimization#protein-design#drug-discovery#computational-biology#ai-research
Read Original →via arXiv – CS AI
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