most citedSSRL: Self-Search Reinforcement Learning

1 citations · 1 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CE2026

Endowing Molecular Language with Geometry Perception via Modality Compensation for High-Throughput Quantum Hamiltonian Prediction

Zhenzhong Wang, Yongjie Hou, Chenggong Huang +3

The quantum Hamiltonian is a fundamental property that governs a molecule's electronic structure and behavior, and its calculation and prediction are paramount in computational che…

cs.CR2025

From Retrieval to Reasoning: A Framework for Cyber Threat Intelligence NER with Explicit and Adaptive Instructions

Jiaren Peng, Hongda Sun, Xuan Tian +3

The automation of Cyber Threat Intelligence (CTI) relies heavily on Named Entity Recognition (NER) to extract critical entities from unstructured text. Currently, Large Language Mo…

cs.AI2025

PEAR: Phase Entropy Aware Reward for Efficient Reasoning

Chen Huang, Wei Lu, Wenxuan Zhang

Large Reasoning Models (LRMs) have achieved impressive performance on complex reasoning tasks by generating detailed chain-of-thought (CoT) explanations. However, these responses a…

cs.CL20251 cited

SSRL: Self-Search Reinforcement Learning

Yuchen Fan, Kaiyan Zhang, Heng Zhou +15

We investigate the potential of large language models (LLMs) to serve as efficient simulators for agentic search tasks in reinforcement learning (RL), thereby reducing dependence o…

cs.HC2025

MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation

Yi Kong, Dianxi Shi, Guoli Yang +4

The recent advancement of autonomous agents powered by Large Language Models (LLMs) has demonstrated significant potential for automating tasks on mobile devices through graphical…

cs.CL2025

Through the Valley: Path to Effective Long CoT Training for Small Language Models

Renjie Luo, Jiaxi Li, Chen Huang +1

Long chain-of-thought (CoT) supervision has become a common strategy to enhance reasoning in language models. While effective for large models, we identify a phenomenon we call Lon…