3 papers
cs.CL2026
What do Language Models Learn and When? The Implicit Curriculum Hypothesis
Emmy Liu, Kaiser Sun, Millicent Li +4
Large language models (LLMs) can perform remarkably complex tasks, yet the fine-grained details of how these capabilities emerge during pretraining remain poorly understood. Scalin…
cs.SE2026
Hybrid-Gym: Training Coding Agents to Generalize Across Tasks
Yiqing Xie, Emmy Liu, Gaokai Zhang +7
When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE-Bench. In contrast, in real use, these agents solve more v…
cs.CL2025
Midtraining Bridges Pretraining and Posttraining Distributions
Emmy Liu, Graham Neubig, Chenyan Xiong
Midtraining, the practice of mixing specialized data with more general pretraining data in an intermediate training phase, has become widespread in language model development, yet…