1 citations · 1 across the 2 of their papers we have counts for
4 papers
DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing
Hongzhi Zhang, Yuanze Hu, Tinghai Zhang +9
The evolution of Large Language Models (LLMs) towards autonomous agents has catalyzed progress in Deep Research. While retrieval capabilities are well-benchmarked, the post-retriev…
Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling
Qi Wang, Hongzhi Zhang, Jia Fu +12
Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In thi…
Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning
Jia Fu, Xinyu Yang, Hongzhi Zhang +5
Precise, correct feedback is crucial for effectively training large language models (LLMs) in code reinforcement learning. However, synthesizing high-quality test cases remains a p…
RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning
Hongzhi Zhang, Jia Fu, Jingyuan Zhang +4
Reinforcement learning (RL) for large language models is an energy-intensive endeavor: training can be unstable, and the policy may gradually drift away from its pretrained weights…