# ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models

Authors: Zhaoyan Jia, Chengwei Zhou, Gourav Datta, Massoud Pedram.

Branches rollouts from uncertain intermediate states and reuses their shared textual prefixes. Local outcome comparisons assign credit to reasoning segments, testing whether more targeted exploration improves diffusion-model reasoning with fewer generated training tokens.

First public: 2026-10-14.
ArXiv initial submission: 2026-10-14.

[Original paper](https://arxiv.org/abs/2610.19408) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.19408/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.19408/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.19408/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.19408/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.

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.