Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL [R]

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• Masked Diffusion (MDLM) language models have been shown to outperform traditional autoregressive (AR) language models in various tasks, including generating coherent and diverse text across different domains.
• The MDLM approach allows for any-order denoising, enabling the model to learn from multiple conditioning directions simultaneously. This leads to more consistent and contextually appropriate outputs compared to AR models that are constrained by a sequential generation mechanism.

The success of MDLMs in tasks like text generation demonstrates their potential as robust world models for reinforcement learning (RL) applications, particularly those involving agent interaction where coherence is crucial. Their ability to generate coherent rollouts without the need for prefix consistency can be leveraged to improve task performance and reduce the risk of model collapse.

Key takeaways:
– MDLMs outperform AR models in terms of metrics like BLEU-1, ROUGE-L, and MAUVE across various domains.
– They achieve these improvements with fewer parameters than traditional models.
– The method is also effective for zero-shot transfer learning to new tasks or environments.

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