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The Seed LLM team is dedicated to advancing the next generation of LLMs and solving fundamental challenges in LLM development. Our areas of focus include model pretraining, posttraining, inference, memory capabilities, learning, interpretability and other related directions. We dive deep into the latest technologies and create comprehensive solutions from concept to completion. In our endeavor to adopt LLMs in real-life scenarios, we explore methods to enhance applications through technological innovation.
Latest advancements

Seed2.0

Seed2.0
Comprehensive Upgrade to Multimodal Understanding, with Significant Enhancement in LLM and Agent Performance

Seed1.8

Seed1.8
A general-purpose agent model capable of efficiently and accurately completing tasks in complex workflows.

Seed Diffusion Preview

Seed Diffusion Preview
A large scale language model based on discrete-state diffusion, specializing in code generation, achieves an inference speed of 2,146 token/s, a 5.4x improvement over autoregressive models of comparable size.

Selected papers

Aug 4, 2025
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference
Computation and Language
May 17, 2025
Model Merging in Pre-training of Large Language Models
LLM
Apr 2, 2025
Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback
Machine Learning
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Research Scientist in Large Language Model
San Jose
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Research Scientist, Reinforcement Learning
San Jose
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Research Scientist in Large Language Model, Seed-PhD Graduates- 2026 Start
San Jose
Campus Recruitment
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Student Researcher [Seed LLM Post Training – Reward Modeling] - 2026 Start (PhD)
Seattle/San Jose
Internship
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Student Researcher (Seed - LLM Post-training) - 2025 Start (PhD)
Seattle/San Jose
Internship
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