3 citations · 3 across the 4 of their papers we have counts for
4 papers
How Far Is Document Parsing from Solved? PureDocBench: A Source-Traceable Benchmark across Clean, Degraded, and Real-World Settings
Zhiheng Li, Zongyang Ma, Jiaxian Chen +12
Document parsing models are improving rapidly, but existing benchmarks are still insufficient for a comprehensive evaluation of their capabilities. We introduce PureDocBench, a ben…
Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning
Zhiyin Yu, Bo Zhang, Qibin Hou +3
Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entropy-based rewards. However, the…
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Zhiyin Yu, Yuchen Mou, Juncheng Yan +17
Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learni…
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study
Bowen Li, Wenhan Wu, Ziwei Tang +13
Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isola…