# Why Deterministic PRM Guidance Underperforms in Discrete Diffusion Reasoning

Authors: Yan Zhan, Shaobo Liu, Zhijun Gao.

Compares process-reward guidance with independent sampling and final-answer reranking at matched forward-pass budgets. In the tested diffusion models, early pruning removes promising candidates and intermediate-state scorers can be poor final judges; the generated solutions remain textual.

First public: 2026-09-28.
ArXiv initial submission: 2026-09-28.

[Original paper](https://arxiv.org/abs/2609.35472) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.35472/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.35472/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.35472/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.35472/record.json)

## Approaches

- [Alternative generation methods](https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/): Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.