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Tyler Griggs

Tyler Griggs has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, GPU & parallel computing; often with Matei Zaharia, Ion Gabriel Stoica, Joseph E. Gonzalez.

3 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 3 · 11 works · 41 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 238 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #7209

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of California, BerkeleyBerkeley · United States2024–2026 · 9 papersEmploymentcurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksHardware and Architecture · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 workGPU & parallel computing · 1 work

 Works by year

2020s

20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Reasoning Models Can Be Effective Without Thinking (2025 · 58 citations)
    Large reasoning models usually write a long chain of thought before answering, and this costs many tokens and adds latency.
  2. LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! (2025 · 56 citations)
    Large reasoning models solve hard problems through long chains of thought with self-checking and backtracking.
  3. MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs (2024 · 51 citations)
    Mixture-of-Experts LLMs keep per-token compute low but need very large amounts of memory. The expert FFNs of Mixtral 8x22B alone take more than 256 GB. This paper targets offline, throughput-oriented batch inference, such as model evaluation, synthetic data generation and data wrangling, when the GPU cannot hold the…

All works

8 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. Reasoning Models Can Be Effective Without ThinkingarXiv:2504.09858Wenjie Ma, Jingxuan He, Charlie Snell, et al.2025 · 58 citations
  2. LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!arXiv:2502.07374Dacheng Li, Shiyi Cao, Tyler Griggs, et al.2025 · 56 citations
  3. MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUsASPLOS 2025Shiyi Cao, Shu Liu, Tyler Griggs, et al.2024 · 51 citations
  4. SkyServe: Serving AI Models across Regions and Clouds with Spot Instances ↗arXiv:2411.01438 · no summary yetZiming Mao et al.2024 · 39 citations
  5. MoE-L ightning : High-Throughput MoE Inference on Memory-constrained GPUs ↗ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · no summary yetShiyi Cao et al.2025 · 28 citations
  6. Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity ↗arXiv:2404.14527 · no summary yetTyler Griggs et al.2024 · 26 citations
  7. Language Models Can Easily Learn to Reason from Demonstrations ↗Findings of the Association for Computational Linguistics: EMNLP 2025 · no summary yetDacheng Li et al.2025 · 14 citations
  8. SkyRL-Agent: Efficient RL Training for Multi-turn LLM Agent ↗arXiv (Cornell University) · no summary yetShiyi Cao et al.2025 · 8 citations

 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 238 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

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