5 papers
BootSeer: Analyzing and Mitigating Initialization Bottlenecks in Large-Scale LLM Training
Rui Li, Xiaoyun Zhi, Jinxin Chi +14
Large Language Models (LLMs) have become a cornerstone of modern AI, driving breakthroughs in natural language processing and expanding into multimodal jobs involving images, audio…
Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets
Jiashi Feng, Xiu Li, Jing Lin +25
Developing embodied AI agents requires scalable training environments that balance content diversity with physics accuracy. World simulators provide such environments but face dist…
Robust LLM Training Infrastructure at ByteDance
Borui Wan, Gaohong Liu, Zuquan Song +32
The training scale of large language models (LLMs) has reached tens of thousands of GPUs and is still continuously expanding, enabling faster learning of larger models. Accompanyin…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
Jiazhan Feng, Shijue Huang, Xingwei Qu +6
While reasoning models (e.g., DeepSeek R1) trained with reinforcement learning (RL), excel in textual reasoning, they struggle in scenarios requiring structured problem-solving, su…