README.md

---
license: apache-2.0
library_name: peft
base_model: GSAI-ML/LLaDA-8B-Instruct
pipeline_tag: text-generation
tags:
- reinforcement-learning
- diffusion-llm
- block-r1
---

# Block-R1

This repository contains model checkpoints (LoRA adapters) for **Block-R1**, a benchmark for multi-domain reinforcement learning with block-based diffusion large language models (dLLMs).

## Description

Block-R1 is designed to enhance block-based reasoning generation in diffusion LLMs. It investigates the role of block size from a domain conflict perspective during reinforcement learning (RL) post-training. The benchmark covers diverse domains including code, mathematics, puzzles, and general knowledge.

Key components include:
- **Block-R1-41K Dataset:** A dataset constructed with optimized training block sizes for multi-domain RL.
- **b1 Method:** A dynamic-size reasoning block method for dLLMs.
- **RL Framework:** Support for multiple RL algorithms for diffusion models such as Diffusion-GRPO, WD1, GDPO, and more.

## Resources

- **Paper:** [Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models](https://huggingface.co/papers/2605.11726)
- **Code:** [GitHub Repository](https://github.com/YanJiangJerry/Block-R1)
- **Dataset:** [Block-R1 Dataset](https://huggingface.co/datasets/dLLM-R1/Block-R1)

## Model Information

These weights are LoRA adapters trained on top of the [LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/LLaDA-8B-Instruct) backbone. For detailed usage, training, and evaluation scripts, please refer to the official repository.

## Citation

If you use this benchmark or the associated methods, please cite the following work:

```bibtex
@article{jiang2026breakblock,
  title={{Break the Block: Dynamic-size Reasoning Blocks for Diffusion Large Language Models via Monotonic Entropy Descent with Reinforcement Learning}},
  author={Jiang, Yan and Qiu, Ruihong and Huang, Zi},
  journal={arXiv preprint arXiv:2605.02263},
  year={2026}
}
```