---
base_model:
- GSAI-ML/LLaDA-8B-Instruct
datasets:
- TIGER-Lab/AceCode-87K
license: mit
pipeline_tag: text-generation
library_name: transformers
---

# ESPO: Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective

This repository contains Post-Training Full models on code tasks based on LLaDA-8B-Instruct for the paper [Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective](https://huggingface.co/papers/2512.03759).

ESPO (ELBO-based Sequence-level Policy Optimization) is a principled reinforcement learning framework for Diffusion Large Language Models (dLLMs). Unlike traditional autoregressive RL methods (e.g., GRPO) that rely on token-level likelihoods, ESPO views the **entire sequence generation as a single action** and leverages the **ELBO** as a tractable proxy for sequence-level likelihood. This design resolves the fundamental mismatch between RL and the non-autoregressive nature of dLLMs.

ESPO introduces:
- **Sequence-level optimization** for diffusion LLMs via the ELBO objective.
- **Per-token normalized ratio estimation** and **robust KL regularization** for stable large-scale training.
- **Consistent gains** across math, coding, and planning benchmarks.

**Project Page**: [https://jingyangou.github.io/ESPO-Demo/](https://jingyangou.github.io/ESPO-Demo/)
**Code**: [https://github.com/ML-GSAI/ESPO](https://github.com/ML-GSAI/ESPO)

<div style="display: flex; justify-content: center; flex-wrap: wrap;">
    <img src="https://github.com/ML-GSAI/ESPO/raw/main/fig/sudoku_ablation_1_smoothed-page1.png" style="width: 49%" />
    <img src="https://github.com/ML-GSAI/ESPO/raw/main/fig/sudoku_kl_ablation_smoothed.png" style="width: 49%" />
</div>

## Quickstart

We release ESPO-fine-tuned checkpoints built on LLaDA-8B-Instruct. ESPO-Code is released as a full fine-tuned model (no LoRA). ESPO-GSM8K, ESPO-Math, ESPO-Countdown, and ESPO-Sudoku are provided as LoRA adapters, which can be loaded on top of the base LLaDA-8B-Instruct model for lightweight and efficient fine-tuning.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Note: 'eval.generate_utils' is part of the original ESPO GitHub repository.
# You might need to clone the repository (https://github.com/ML-GSAI/ESPO)
# and add its root directory to your Python path to import `eval.generate_utils`.
from eval.generate_utils import generate 

base_model_path = 'GSAI-ML/LLaDA-8B-Instruct'
peft_model_path = 'GSAI-ML/ESPO-Math' # Example: change to ESPO-Code for the full model
tokenizer = AutoTokenizer.from_pretrained(base_model_path)
model = AutoModelForCausalLM.from_pretrained(
    base_model_path, trust_remote_code=True,torch_dtype="bfloat16", device_map="cuda")
peft_model = PeftModel.from_pretrained(model, peft_model_path, device_map="cuda")
prompt = "The point $(0,0)$ is reflected over the vertical line $x=1$.  When its image is then reflected over the line $y=2$, what is the resulting point?

Write your answer in the form $(x, y)$ where $x$ and $y$ are real numbers."
messages = [{"role": "user", "content": prompt}]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
output_ids = generate(peft_model, input_ids,tokenizer, steps=128,    gen_length=256, temperature=0.9,remasking="low_confidence",)
output_text = tokenizer.batch_decode(output_ids[:, input_ids.shape[1]:], skip_special_tokens=True)[0]
print(output_text)
```

## Citation
If you find ESPO useful in your research, please consider citing our paper:

```bibtex
@article{ou2025principledrldiffusionllms,
      title={Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective}, 
      author={Jingyang Ou and Jiaqi Han and Minkai Xu and Shaoxuan Xu and Jianwen Xie and Stefano Ermon and Yi Wu and Chongxuan Li},
      journal={arXiv preprint arXiv:2512.03759},
      year={2025},
}
```