8 citations · 20 across the 16 of their papers we have counts for
17 papers · 1 filter
General-Reasoner: Advancing LLM Reasoning Across All Domains
Xueguang Ma, Qian Liu, Dongfu Jiang +3
Reinforcement learning (RL) has recently demonstrated strong potential in enhancing the reasoning capabilities of large language models (LLMs). Particularly, the "Zero" reinforceme…
Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate
Yubo Wang, Xiang Yue, Wenhu Chen
Supervised Fine-Tuning (SFT) is commonly used to train language models to imitate annotated responses for given instructions. In this paper, we propose Critique Fine-Tuning (CFT),…
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale
Jarvis Guo, Tuney Zheng, Yuelin Bai +7
Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained…
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
Xiang Yue, Tianyu Zheng, Yuansheng Ni +10
This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal mo…
LongIns: A Challenging Long-context Instruction-based Exam for LLMs
Shawn Gavin, Tuney Zheng, Jiaheng Liu +6
The long-context capabilities of large language models (LLMs) have been a hot topic in recent years. To evaluate the performance of LLMs in different scenarios, various assessment…
MAmmoTH2: Scaling Instructions from the Web
Xiang Yue, Tuney Zheng, Ge Zhang +1
Instruction tuning improves the reasoning abilities of large language models (LLMs), with data quality and scalability being the crucial factors. Most instruction tuning data come…