ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs
Yang Yang, Qinyu Zhao, Mouxiang Chen, Xiaohui Li, Lixin Gu, Wenhai Wang, Hongjie Zhang, Wenwei Zhang
cs.CV, cs.CL
Submitted: 2026-08-04
Comments: 14 pages, 4 figures
Code: https://github.com/YangYangGirl/ParVL
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Qwen3-VL Technical Report
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Distilling the Knowledge in a Neural Network
- Training Compute-Optimal Large Language Models
- Scaling Laws for Neural Language Models
- ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
- OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models
- Ovis: Structural Embedding Alignment for Multimodal Large Language Model
- Learning Adaptive Parallel Reasoning with Language Models
- HoliTom: Holistic Token Merging for Fast Video Large Language Models
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models
- InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts
- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
- Scaling Latent Reasoning via Looped Language Models
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