2 citations · 2 across the 6 of their papers we have counts for
8 papers
miniTE: Diagnosing and Bridging the Execution Gap in Native NVFP4 Reinforcement Learning
Zhen Li, Mingfa Feng, Yiming Zhang +6
Native NVFP4 promises to accelerate reinforcement learning (RL) for large language models (LLMs), but achieving stable learning across separate training and sampling engines remain…
FusionEval: Benchmarking Model Fusion for Large Language Models
Shuo Cai, Yanggan Gu, Yuhang Liu +8
Model fusion aims to integrate the capabilities of multi-expert models into a single model. Existing evaluations offer limited coverage of reasoning with long chains of thought and…
BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models
Junyu Chen, Jungang Li, Jing Xiong +11
Large language model inference is often bounded by memory footprint and bandwidth in resource-constrained deployments, making quantization fundamental to efficient serving. While p…
A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models
Wenjun Wang, Shuo Cai, Congkai Xie +7
The immense computational cost of training Large Language Models (LLMs) presents a major barrier to innovation. While FP8 training offers a promising solution with significant theo…
Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models
Zeyu Liu, Yuhang Liu, Guanghao Zhu +9
Recent advancements in large language models (LLMs) have demonstrated substantial progress in reasoning capabilities, such as DeepSeek-R1, which leverages rule-based reinforcement…
Quantization Meets Reasoning: Exploring and Mitigating Degradation of Low-Bit LLMs in Mathematical Reasoning
Zhen Li, Yupeng Su, Songmiao Wang +8
Low-bit post-training quantization (PTQ) is a practical route to deploy reasoning-capable LLMs under tight memory and latency budgets, yet it can markedly impair mathematical reaso…
Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning
Zhen Li, Yupeng Su, Runming Yang +5
Large language models have achieved significant advancements in complex mathematical reasoning benchmarks, such as MATH. However, their substantial computational requirements prese…
InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
Congkai Xie, Shuo Cai, Wenjun Wang +17
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as…