# Review, Remask, Refine (R3): Process-Guided Block Diffusion for Text Generation

arXiv: https://arxiv.org/abs/2507.08018 · HTML: https://mlsanity.com/paper/2507.08018/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [The Lessons of Developing Process Reward Models in Mathematical Reasoning](https://mlsanity.com/paper/2501.07301/) (2025)
- [ProcessBench: Identifying Process Errors in Mathematical Reasoning](https://mlsanity.com/paper/2412.06559/) (2024)
- [Remasking Discrete Diffusion Models with Inference-Time Scaling](https://mlsanity.com/paper/2503.00307/) (2025)

## Built on by (most cited first, 3 of 3)

- [CORE: Context-Robust Remasking for Diffusion Language Models](https://mlsanity.com/paper/2602.04096/) (2026)
- [Reversible Diffusion Decoding for Diffusion Language Models](https://mlsanity.com/paper/2602.00150/) (2026)
- [Context-Aware Cluster Decoding: Semantic Anchor-Driven Coherence in dMLLMs](https://mlsanity.com/paper/2608.22367/) (2026)

## Abstract (arXiv)

A key challenge for iterative text generation is enabling models to efficiently identify and correct their own errors. We propose Review, Remask, Refine (R3), a relatively simple yet elegant framework that requires no additional model training and can be applied to any pre-trained masked text diffusion model (e.g., LLaDA or BD3-LM). In R3, a Process Reward Model (PRM) is utilized for the Review of intermediate generated blocks. The framework then translates these PRM scores into a Remask strategy: the lower a block's PRM score, indicating potential mistakes, the greater the proportion of tokens within that block are remasked. Finally, the model is compelled to Refine these targeted segments, focusing its efforts more intensively on specific sub-optimal parts of past generations, leading to improved final output.