3 papers
cs.CL2025
Parallel Loop Transformer for Efficient Test-Time Computation Scaling
Bohong Wu, Mengzhao Chen, Xiang Luo +9
Large Language Models (LLMs) are powerful but often too slow and costly for real-world use during inference. Looped transformers save on parameters by reusing the same weights for…
cs.LG2025
MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production
Chao Jin, Ziheng Jiang, Zhihao Bai +16
We present MegaScale-MoE, a production system tailored for the efficient training of large-scale mixture-of-experts (MoE) models. MoE emerges as a promising architecture to scale l…
cs.CL2025
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…