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cs.CL2026
TRiMS: Real-Time Tracking of Minimal Sufficient Length for Efficient Reasoning via RL
Tingcheng Bian, Jinchang Luo, Mingquan Cheng +5
Large language models achieve breakthroughs in complex reasoning via long chain-of-thought sequences. However, this often leads to severe reasoning inflation, causing substantial c…
cs.CL2025
GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning
Jinchang Luo, Mingquan Cheng, Fan Wan +7
Reinforcement learning has recently shown promise in improving retrieval-augmented generation (RAG). Despite these advances, its effectiveness in multi-hop question answering (QA)…
cs.CL2024★ 2 cited
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation
Bolei He, Nuo Chen, Xinran He +4
Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models (LLMs) by incorporating extensive knowledge retrieved from external sources. However, such approac…