🧑‍🔬 Bryan Dai

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning (2025 · 104 citations)
   Logic-RL tests whether the rule-based reinforcement learning (RL) approach behind DeepSeek-R1 can be reproduced with a smaller model, and what training data supports it best.
2. Scaling Laws for Code: Every Programming Language Matters (2026 · 2 citations)
   Classic scaling laws treat code as a single, language-agnostic domain. In practice, programming languages (PLs) contribute unequally during pre-training, so loss predictions for multilingual code LLMs can be inaccurate. This study runs more than 1000 pre-training experiments from scratch, costing roughly 336,000 H800…
3. Scaling is Not All You Need: Clinical-Oriented Reinforcement Learning Makes Parameter-Efficient Clinical Reasoning (2026)
   Strong medical reasoning in LLMs usually depends on very large models and costly hand-annotated chain-of-thought data.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement LearningarXiv:2502.14768Tian Xie, Zitian Gao, Qingnan Ren, et al.2025 · 104 citations
2. Scaling Laws for Code: Every Programming Language MattersFindings of the Association for Computational LinguisticsJian Yang, Shuyue Guo, Linzheng Chai, et al.2026 · 2 citations
3. Scaling is Not All You Need: Clinical-Oriented Reinforcement Learning Makes Parameter-Efficient Clinical ReasoningFindings of the Association for Computational LinguisticsChi Liu, Yan Shu, Mengzhuo Chen, et al.2026
4. LoopCoder: Scaling Code Intelligence via Looped Language Models ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetJian Yang et al.2026

🤝 Co-authors here

11 co-authors with a page, the most papers together first.

• Aishan Liu
• Chi Liu
• Hongming Piao
• Jian Yang
• Joey Tianyi Zhou
• Linzheng Chai
• Shuyue Guo
• Wei Zhang
• Xianglong Liu
• Xin Zhao
• Zhoujun Li

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
• Findings of the Association for Computational Linguistics: ACL 2026

🔗 Cited by

Papers of our library cite their works 100 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated 1 November 2026

This page in Markdown → https://md.research.elamur.ai/free/en/authors/bryan-dai/