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Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages
arXiv – CS AI|Vishnu Teja Kunde, Fatemeh Doudi, Mahdi Farahbakhsh, Dileep Kalathil, Krishna Narayanan, Jean-Francois Chamberland|
🤖AI Summary
Researchers developed a new reinforcement learning approach for training diffusion language models that uses entropy-guided step selection and stepwise advantages to overcome challenges with sequence-level likelihood calculations. The method achieves state-of-the-art results on coding and logical reasoning benchmarks while being more computationally efficient than existing approaches.
Key Takeaways
- →New RL method for diffusion language models avoids bias from surrogate likelihoods by using exact policy gradients.
- →Entropy-guided step selection and one-step denoising rewards make the approach computationally efficient.
- →Method treats diffusion sequence generation as a finite-horizon Markov decision process over denoising trajectories.
- →Achieves state-of-the-art performance on coding and logical reasoning benchmarks.
- →Outperforms existing RL post-training approaches for diffusion language models on mathematical reasoning tasks.
#reinforcement-learning#diffusion-models#language-models#policy-gradient#machine-learning#research#benchmark#coding#reasoning
Read Original →via arXiv – CS AI
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