CaRE Compute-aware Remasking Evaluation Protocol for Masked Diffusion Language Models
Standardizing compute and stochasticity to expose hidden artifacts in masked diffusion language model evaluation.
Masked diffusion language models (MDLMs) are advancing rapidly, yet the evaluation standards needed to reliably interpret their progress have not kept pace. Despite MDLMs becoming competitive with autoregressive language models, seven recent remasking papers evaluate under incomp...
ArXiv CS.AI
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The CaRE framework exposes that many reported gains in masked diffusion language models are actually artifacts of inconsistent compute and stochasticity settings rather than true algorithmic breakthroughs. This research underscores a critical need for standardized evaluation protocols to ensure the scientific integrity and reproducibility of non-autoregressive generative models.
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