47 citations · 105 across the 52 of their papers we have counts for
9 papers · 1 filter
RLoop: An Self-Improving Framework for Reinforcement Learning with Iterative Policy Initialization
Zeng Zhiyuan, Jiashuo Liu, Zhangyue Yin +3
While Reinforcement Learning for Verifiable Rewards (RLVR) is powerful for training large reasoning models, its training dynamics harbor a critical challenge: RL overfitting, where…
UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
Haoming Wang, Haoyang Zou, Huatong Song +109
The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…
FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction
Zhiyuan Zeng, Jiashuo Liu, Siyuan Chen +28
Future prediction is a complex task for LLM agents, requiring a high level of analytical thinking, information gathering, contextual understanding, and decision-making under uncert…
Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving
Luoxin Chen, Jinming Gu, Liankai Huang +33
LLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving d…
First Return, Entropy-Eliciting Explore
Tianyu Zheng, Tianshun Xing, Qingshui Gu +10
Reinforcement Learning from Verifiable Rewards (RLVR) improves the reasoning abilities of Large Language Models (LLMs) but it struggles with unstable exploration. We propose FR3E (…
FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
Zhouliang Yu, Ruotian Peng, Keyi Ding +10
Formal mathematical reasoning remains a critical challenge for artificial intelligence, hindered by limitations of existing benchmarks in scope and scale. To address this, we prese…
Aligning Instruction Tuning with Pre-training
Yiming Liang, Tianyu Zheng, Xinrun Du +12
Instruction tuning enhances large language models (LLMs) to follow human instructions across diverse tasks, relying on high-quality datasets to guide behavior. However, these datas…
GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy for Large Language Models
Leyan Wang, Yonggang Jin, Tianhao Shen +9
As large language models (LLMs) continue to develop and gain widespread application, the ability of LLMs to exhibit empathy towards diverse group identities and understand their pe…
PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents
Junjie Wang, Yuxiang Zhang, Minghao Liu +19
Recent advancements in large multimodal models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks. However, persistent ch…