Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation
📰 ArXiv cs.AI
Learn how to transform autoregressive language models into diffusion language models via on-policy distillation, improving data efficiency
Action Steps
- Transform an autoregressive language model into a diffusion language model by replacing causal attention with bidirectional attention
- Apply on-policy distillation to transfer knowledge from the original model to the new diffusion model
- Train the resulting model using a diffusion language model objective
- Evaluate the performance of the new model on a target task
- Fine-tune the model as needed to achieve optimal results
Who Needs to Know This
NLP researchers and engineers can benefit from this technique to develop more efficient language models, while data scientists and ML engineers can apply this knowledge to improve their model's performance
Key Insight
💡 On-policy distillation can effectively transfer knowledge from autoregressive language models to diffusion language models, reducing the need for large amounts of training data
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🚀 Improve data efficiency in language models with autoregressive-to-diffusion transformation via on-policy distillation! 🤖
Key Takeaways
Learn how to transform autoregressive language models into diffusion language models via on-policy distillation, improving data efficiency
Full Article
Title: Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation
Abstract:
arXiv:2606.06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs). Rather than pretraining from scratch, prior work replaces the causal attention in ARLMs with bidirectional attention and then trains the resulting model using a DLM objective. However, these approaches incur two distribution shifts. First, transitioning from a next-token prediction objective to a DLM objective can discard knowledge acquired by the
Abstract:
arXiv:2606.06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs). Rather than pretraining from scratch, prior work replaces the causal attention in ARLMs with bidirectional attention and then trains the resulting model using a DLM objective. However, these approaches incur two distribution shifts. First, transitioning from a next-token prediction objective to a DLM objective can discard knowledge acquired by the
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