2 citations · 4 across the 5 of their papers we have counts for
5 papers
ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression
Tingcheng Bian, Yuzhe Zhang, Jing Jin +5
Large reasoning models (LRMs) achieve strong performance via extended chain-of-thought (CoT) reasoning, yet suffer from excessive token consumption and high inference latency. Exis…
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…
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)…
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…
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference
Huiqiang Zhong, Cunxiang Yin, Xiaohui Wu +2
Urban air pollution has become a major environmental problem that threatens public health. It has become increasingly important to infer fine-grained urban air quality based on exi…