4 citations · 6 across the 6 of their papers we have counts for
6 papers
HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning
Weiqi Wang, Xin Liu, Binxuan Huang +13
RLVR has become a standard recipe for training LLMs on reasoning tasks with verifiable outcomes, but when rollout generation dominates the cost, efficiency hinges on which prompts…
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations
Fengran Mo, Yifan Gao, Chuan Meng +9
The rapid advancement of conversational search systems revolutionizes how information is accessed by enabling the multi-turn interaction between the user and the system. Existing c…
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training
Yuchen Zhuang, Jingfeng Yang, Haoming Jiang +16
Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce ne…
END: Early Noise Dropping for Efficient and Effective Context Denoising
Hongye Jin, Pei Chen, Jingfeng Yang +11
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, they are often distracted by irrelevant or…
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Yilun Jin, Zheng Li, Chenwei Zhang +19
Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are com…
Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
Kewei Cheng, Jingfeng Yang, Haoming Jiang +9
Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), mo…