--- title: "Who cites LLaDA 1.5: Variance-Reduced — 42 citing papers" description: "42 papers of the Elamur Research library cite «LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models»…" canonical: "https://research.elamur.ai/free/en/papers/zhu-2026-llada-1-5-variance-reduced/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhu-2026-llada-1-5-variance-reduced/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LLaDA 1.5: Variance-Reduced Preference…](https://md.research.elamur.ai/free/en/papers/zhu-2026-llada-1-5-variance-reduced/) / cited by # Who cites «LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models» 42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · [← back to «LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models»](https://md.research.elamur.ai/free/en/papers/zhu-2026-llada-1-5-variance-reduced/) 1. [**A Survey on Latent Reasoning arXiv:2507.06203**Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-survey-latent-reasoning/) 2. **[d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/pan-2026-d-treerpo-more-reliable-policy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leyi Pan, Shuchang Tao, Yunpeng Zhai, et al.*2026 · 3 citations* 3. **[Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/zhong-2026-beyond-hard-masks-progressive-token/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linhao Zhong, Linyu Wu, Bozhen Fang, et al.*2026 · 2 citations* 4. **[Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing](https://md.research.elamur.ai/free/en/papers/long-2026-focus-dllm-accelerating-long-context/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingkun Long, Yushi Huang, Shihao Bai, et al.*2026 · 2 citations* 5. **[On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration](https://md.research.elamur.ai/free/en/papers/zhu-2026-policy-self-distillation-efficient-diffusion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Huaisheng Zhu, MingYu Liu, Junze Liu, et al.*2026 · 2 citations* 6. **[One Token Is Enough: Improving Diffusion Language Models with a Sink Token](https://md.research.elamur.ai/free/en/papers/zhang-2026-one-token-is-enough-improving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihou Zhang, Zheyong Xie, Li Zhong, et al.*2026 · 2 citations* 7. **[Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration](https://md.research.elamur.ai/free/en/papers/zhong-2026-efficient-self-evaluation-diffusion-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linhao Zhong, Linyu Wu, Wen Wang, et al.*2026 · 1 citation* 8. **[Energy Matching based Preference Learning for Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/shankar-2026-energy-matching-based-preference-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Shiv Shankar*2026 · 1 citation* 9. **[A Universal Avoidance Method for Diverse Multi-branch Generation](https://md.research.elamur.ai/free/en/papers/park-2026-universal-avoidance-method-diverse-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kyeongman Park, Minha Jhang, Kyomin Jung*2026* 10. **[CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit](https://md.research.elamur.ai/free/en/papers/wang-2026-creditdecoding-accelerating-parallel-decoding-diffusion/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kangyu Wang, Zhiyun Jiang, Haibo Feng, et al.*2026* 11. **[PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding](https://md.research.elamur.ai/free/en/papers/park-2026-pure-post-hoc-unlocking-refinement/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yangryeol Park, Kunhui Lee, Hanback Choi, et al.*2026* 31 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhu-2026-llada-1-5-variance-reduced/) — LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models