dOPSD: On-Policy Self-Distillation for Diffusion Language Models
📰 ArXiv cs.AI
Learn how to improve diffusion language models with on-policy self-distillation, a technique that enhances post-training reasoning without exposure bias
Action Steps
- Implement on-policy self-distillation for diffusion language models using dOPSD
- Apply dOPSD to your existing diffusion language model to reduce exposure bias
- Compare the performance of your model with and without dOPSD
- Fine-tune your model using dOPSD and evaluate its reasoning capabilities
- Test dOPSD on different diffusion language models and tasks to assess its generalizability
Who Needs to Know This
NLP engineers and researchers can benefit from this technique to improve the performance of their diffusion language models, especially when fine-tuning is challenging
Key Insight
💡 On-policy self-distillation can enhance post-training reasoning in diffusion language models without suffering from exposure bias
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🚀 Improve diffusion language models with on-policy self-distillation! 🤖
Key Takeaways
Learn how to improve diffusion language models with on-policy self-distillation, a technique that enhances post-training reasoning without exposure bias
Full Article
Title: dOPSD: On-Policy Self-Distillation for Diffusion Language Models
Abstract:
arXiv:2607.04428v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising a masked sequence, offering a parallel alternative to autoregressive models, but eliciting strong reasoning through post-training remains difficult: supervised fine-tuning is off-policy and suffers from exposure bias, while reinforcement learning gives only sparse, sequence-level rewards and is hard to apply without tractable sequence likelihoods. On-policy self-distil
Abstract:
arXiv:2607.04428v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising a masked sequence, offering a parallel alternative to autoregressive models, but eliciting strong reasoning through post-training remains difficult: supervised fine-tuning is off-policy and suffers from exposure bias, while reinforcement learning gives only sparse, sequence-level rewards and is hard to apply without tractable sequence likelihoods. On-policy self-distil
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