activity
20202026
most citedEfficient Prompting Methods for Large Language Models: A Survey

18 citations · 43 across the 76 of their papers we have counts for

collaborators

78 papers

cs.CL2026

Better Decomposition, Free Aggregation: A Synthesizer-Folding Framework for Multilingual Multi-Hop Question Answering

Yilin Wang, Yuchun Fan, Weidong Bao +5

Multilingual retrieval-augmented generation (mRAG) equips large language models with access to globally distributed external knowledge for complex multilingual question answering.…

cs.LG2026

Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning

Yifu Huo, Shunjie Xing, Chenglong Wang +8

Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments. A promising solution to this challenge is credit assignment, which aim…

cs.LG2026

RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction

Chenglong Wang, Ziming Zhu, Yifu Huo +9

Recent advances in reward modeling show a paradigm shift from discriminative reward models to generative reward models. However, despite their strong capabilities in response ranki…

cs.CL2026

DF-ReAG: Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation

Jiaoyang Li, Junhao Ruan, Shengwei Tang +4

Large language models (LLMs) often generate inaccurate answers due to their reliance on static internal knowledge. Retrieval-augmented generation (RAG) addresses this limitation by…

cs.LG2026

FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models

Kaiyang Ye, Yuan Ge, Junxiang Zhang +8

While on-policy distillation (OPD) effectively addresses sparse rewards and exposure bias in large language model post-training, its extension to flow models remains underexplored.…

cs.CL2026

ToFu: A White-Box, Token-Efficient Agent Harness for Researchers

Junhao Ruan, Yuan Ge, Bei Li +7

Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness…