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.
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/ Code: https://github.com/ML-GSAI/ESPO
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.
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:
@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},
}