Lingjun Liu
Lingjun Liu has 2 papers in our library, in 2025, most of them on Language models & pretraining, Reasoning; often with Tiantian Fan, Xin Liu, Yu Yue.
OpenAlex · ORCID · h-index 17 · 72 works · 2,323 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 4,260 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #2775
Affiliations
Concepts, shelves and topics
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksLanguage and Linguistics · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksMathematics · 1 workComputer Vision and Pattern Recognition · 1 work
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work
Works by year
2020s
Papers
- DAPO: An Open-Source LLM Reinforcement Learning System at Scale (2025 · 634 citations)
Reasoning-focused LLMs such as OpenAI o1 and DeepSeek R1 get much of their ability from large-scale reinforcement learning, but their reports leave out the training details that matter, so others have struggled to reproduce the results. - Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning (2025 · 61 citations)
Seed1.5-Thinking is a reasoning model from ByteDance Seed. It works through an extended chain of thought before giving its answer. The authors identify three factors behind strong reasoning models: training data, a stable RL algorithm and scalable RL infrastructure.
All works
- DAPO: An Open-Source LLM Reinforcement Learning System at ScalearXiv:2503.14476Qiying Yu, Zheng Zhang, Ruofei Zhu, et al.2025 · 634 citations
- VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning TasksarXiv:2504.05118 · original ↗ · no summary yetYu Yue et al.2025 · 65 citations
- Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement LearningarXiv:2504.13914ByteDance Seed, Jiaze Chen, Tiantian Fan, et al.2025 · 61 citations
- UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement LearningarXiv:2509.02544 · original ↗ · no summary yetHaoming Wang et al.2025 · 24 citations
Co-authors here
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