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About this paper
Nikita Mounier, Parsa Idehpour
The Review, Remask, Refine (R3) framework improves iterative text generation by using Process Reward Models (PRMs) to identify and correct errors within masked diffusion models at inference time. Evaluating on a MATH dataset subset, R3 increased problem-solving accuracy from 29.13% to 42.52% over a simple diffusion baseline, while requiring substantially fewer PRM calls than a block-wise best-of-N approach.
Key insights
- Process Reward Models provide the necessary granular feedback at the block level, which is crucial for targeted self-correction in iterative text generation.
- An inference-time framework can effectively leverage pre-trained components to improve text quality from masked diffusion models without requiring computationally intensive model retraining.
- Strategic remasking and refinement, guided by PRM scores and a windowed evaluation approach, balances performance gains with computational efficiency by focusing on problematic segments.
Summary by alphaXiv.
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