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LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models
arXiv – CS AI|Chenxing Wei, Jiazhen Kang, Hong Wang, Jianqing Zhang, Hao Jiang, Xiaolong Xu, Ningyuan Sun, Ying He, F. Richard Yu, Yao Shu, Bo Jiang||7 views
🤖AI Summary
Researchers propose Likelihood-Free Policy Optimization (LFPO), a new framework for improving Diffusion Large Language Models by bypassing likelihood computation issues that plague existing methods. LFPO uses geometric velocity rectification to optimize denoising logits directly, achieving better performance on code and reasoning tasks while reducing inference time by 20%.
Key Takeaways
- →LFPO addresses fundamental limitations in applying reinforcement learning to Diffusion Large Language Models by eliminating the need for exact likelihood computation.
- →The framework uses geometric velocity rectification to directly optimize denoising logits through contrastive updates, providing more precise gradient estimation.
- →LFPO enforces consistency by predicting final solutions from intermediate steps, effectively straightening probability flow.
- →Experiments show LFPO outperforms existing baselines on code generation and mathematical reasoning benchmarks.
- →The method accelerates inference by approximately 20% through reduced diffusion steps while maintaining quality.
#diffusion-models#reinforcement-learning#language-models#optimization#code-generation#mathematical-reasoning#inference-acceleration#machine-learning#research
Read Original →via arXiv – CS AI
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