Patrick Jiang
@patpcj
Ph.D. in CS @UofIllinois | Student Researcher @GoogleResearch
📢(1/15) Introducing s3 — a search-only RL framework for RAG. 🔗arxiv.org/abs/2505.14146 Unlike prior agentic RAG methods, s3 optimizes search alone with a novel reward signal: Gain Beyond RAG (GBR). s3 beats state-of-the-art baselines using just 2.4k training samples.…
You 𝗗𝗢𝗡'𝗧 need so much data to train a Search Agent. Just 2.4k random samples — that's all it takes for our s3. Coming Soon. #LLM #RAG #SearchAgent #EMNLP2025 #NeurIPS2025 #ACL2025NLP #ACL2025 #NAACL2025 #AgenticRAG #AgenticAI #AgenticSearch #SearchAgent
Qwen-3 has released several amazing small models, which will further improve our recent work on DeepRetrieval and Rec-R1. These powerful small models are perfect for university labs to have truly amazing work, even if not have enough computational resources…
Introducing Qwen3! We release and open-weight Qwen3, our latest large language models, including 2 MoE models and 6 dense models, ranging from 0.6B to 235B. Our flagship model, Qwen3-235B-A22B, achieves competitive results in benchmark evaluations of coding, math, general…