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home / papers / 2026 / LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models

LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models

Paper · 2026 · Fengqi Zhu, Rongzhen Wang, Shen Nie et al.

Summary

Masked diffusion models (MDMs) for language cannot compute exact log-likelihoods, so preference alignment methods such as DPO cannot be applied directly. A common workaround is to replace each likelihood with an ELBO estimated by doubly Monte Carlo sampling. The authors show that this estimate is noisy, and that its noise adds bias and variance to both the DPO loss and its gradient. Their analysis traces these errors to a single quantity: the variance of the ELBO-based preference score estimator. They then propose VRPO (Variance-Reduced Preference Optimization), which lowers this variance without introducing bias through three techniques:

  • a larger sampling budget,
  • spreading that budget over distinct timesteps with one masked sample each,
  • antithetic sampling that couples the ELBO estimates of the policy and the reference model.

Applying VRPO to LLaDA 8B Instruct produced LLaDA 1.5, which beats its SFT-only predecessor on math, code and alignment benchmarks. The work matters because it gives a theoretically grounded recipe for aligning diffusion LLMs on general tasks, not only on specialized reasoning tasks.

Key points

  • Training used about 350k preference pairs on LLaDA 8B Instruct. The released model is on Hugging Face as GSAI-ML/LLaDA-1.5.
  • Gains over LLaDA were GSM8K +4.7, HumanEval +3.0, MBPP +1.8, IFEval +4.0 and Arena-Hard +4.3. Math results are competitive with strong MDMs and ARMs.
  • In the ablations, raising the sampling budget n from 1 to 8 cut the score-estimator variance from 44.0 to 1.0 and lifted GSM8K from 80.1 to 83.3.
  • Under a fixed budget, both optimal allocation and antithetic sampling lowered variance. However, large variance drops did not always lead to large benchmark gains.
  • Limitations:
    • training is somewhat more expensive;
    • adaptive timestep scheduling was not studied;
    • extensions to PPO/GRPO are discussed only theoretically, since experiments cover DPO alone.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢Ant GroupHangzhou · China42 works · 1,131 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

Cite

Cite as Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, Ji-Rong Wen and Chongxuan Li. 2026. LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.524

@inproceedings{zhu2026llada,
  title = {{LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models}},
  author = {Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, Ji-Rong Wen and Chongxuan Li},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.524},
  url = {https://doi.org/10.18653/v1/2026.acl-long.524}
}

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