4 papers
From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR
Jia Deng, Jie Chen, Zhipeng Chen +7
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs). Unlike traditiona…
Towards Effective Code-Integrated Reasoning
Fei Bai, Yingqian Min, Beichen Zhang +6
In this paper, we investigate code-integrated reasoning, where models generate code when necessary and integrate feedback by executing it through a code interpreter. To acquire thi…
AutoData: A Multi-Agent System for Open Web Data Collection
Tianyi Ma, Yiyue Qian, Zheyuan Zhang +9
The exponential growth of data-driven systems and AI technologies has intensified the demand for high-quality web-sourced datasets. While existing datasets have proven valuable, co…
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
Shuang Sun, Huatong Song, Yuhao Wang +10
Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information re…