29 citations · 59 across the 22 of their papers we have counts for
31 papers · 1 filter
OpenCompass: A Universal Evaluation Platform for Large Language Models
Maosong Cao, Kai Chen, Haodong Duan +27
In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs). With the…
Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains
Zhonghang Yuan, Zhefan Wang, Fang Hu +7
Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance the reasoning capabilities of large language models (LLMs) in domains such as…
Rectifying LLM Thought from Lens of Optimization
Junnan Liu, Hongwei Liu, Songyang Zhang +1
Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain-of-thought (CoT) prompting, which ena…
CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward
Shudong Liu, Hongwei Liu, Junnan Liu +8
Answer verification is crucial not only for evaluating large language models (LLMs) by matching their unstructured outputs against standard answers, but also serves as the reward m…
When Tools Hurt LLM Reasoning: State-Dependent Belief Revision under External Evidence
Yufeng Zhao, Junnan Liu, Hongwei Liu +4
Tool use is often assumed to monotonically improve reasoning, where external evidence is expected to help when relevant and be ignored when irrelevant. We show that this assumption…
CompassJudger-2: Towards Generalist Judge Model via Verifiable Rewards
Taolin Zhang, Maosong Cao, Alexander Lam +2
Recently, the role of LLM-as-judge in evaluating large language models has gained prominence. However, current judge models suffer from narrow specialization and limited robustness…