Ye Liu is a Senior Research Scientist focused on text and code embedding, retrieval-augmented generation, question answering, and reasoning. She collaborates with researchers and engineers to advance language model capabilities and deliver practical, context-aware AI solutions.
Post-training methods (RLVR, On-policy distillation) are Episode-local Language models are getting better at learning from feedback during post-training. In reinforcement learning with verifiable rewards (RLVR), a model tries a problem, a verifier checks…
Large language model (LLM)-based software engineering (SWE-) agents have recently demonstrated remarkable progress on realistic software engineering tasks such as code review, bug fixing, and repository-level reasoning. Most SWE-agents start from a fresh…
Developers face unique challenges when retrieving code snippets, such as understanding syntax, control flow, and variable dependencies. Enter SFR-Embedding-Code, a groundbreaking family of code embedding models that aims to address these challenges and revolutionize how we retrieve and generate code.
The SFR-Embedding-Mistral marks a significant advancement in text-embedding models, building upon the solid foundations of E5-mistral-7b-instruct and Mistral-7B-v0.1.
