4 papers
Configuration-to-Performance Scaling Law with Neural Ansatz
Huaqing Zhang, Kaiyue Wen, Tengyu Ma
Researchers build scaling laws to forecast the training performance of expensive large-scale runs with larger model size N and data size D. These laws assume that other training hy…
Divide-and-Conquer CoT: RL for Reducing Latency via Parallel Reasoning
Arvind Mahankali, Kaiyue Wen, Tengyu Ma
Long chain-of-thought reasoning (Long CoT) is now fundamental to state-of-the-art LLMs, especially in mathematical reasoning. However, LLM generation is highly sequential, and long…
Fantastic Pretraining Optimizers and Where to Find Them
Kaiyue Wen, David Hall, Tengyu Ma +1
AdamW has long been the dominant optimizer in language model pretraining, despite numerous claims that alternative optimizers offer 1.4 to 2x speedup. We posit that two methodologi…
Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective
Kaiyue Wen, Zhiyuan Li, Jason Wang +3
Training language models currently requires pre-determining a fixed compute budget because the typical cosine learning rate schedule depends on the total number of steps. In contra…