11 citations · 17 across the 5 of their papers we have counts for
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cs.CL2025
Beyond the limitation of a single query: Train your LLM for query expansion with Reinforcement Learning
Shu Zhao, Tan Yu, Anbang Xu
Reasoning-augmented search agents, such as Search-R1, are trained to reason, search, and generate the final answer iteratively. Nevertheless, due to their limited capabilities in r…
cs.CL2025
ParallelSearch: Train your LLMs to Decompose Query and Search Sub-queries in Parallel with Reinforcement Learning
Shu Zhao, Tan Yu, Anbang Xu +3
Reasoning-augmented search agents such as Search-R1, trained via reinforcement learning with verifiable rewards (RLVR), demonstrate remarkable capabilities in multi-step informatio…
cs.CL2024★ 6 cited
In Defense of RAG in the Era of Long-Context Language Models
Tan Yu, Anbang Xu, Rama Akkiraju
Overcoming the limited context limitations in early-generation LLMs, retrieval-augmented generation (RAG) has been a reliable solution for context-based answer generation in the pa…