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- [2505. 10475] Parallel Scaling Law for Language Models - arXiv. org
We theoretically propose a new scaling law and validate it through large-scale pre-training, which shows that a model with P parallel streams is similar to scaling the parameters by O(log P) while showing superior inference efficiency
- Parallel Scaling Law for Language Models - OpenReview
We propose a proof-of-concept scaling approach called parallel scaling (PARSCALE) to validate this hypothesis on language models The core idea is to increase the number of parallel streams while making the input transformation and output aggregation learnable
- Parallel Scaling Law for Language Model - GitHub
We introduce the third scaling paradigm for scaling LLMs: leverages parallel computation during both training and inference time (Parallel Scaling, or ParScale)
- NeurIPS Poster Parallel Scaling Law for Language Models
This research introduces a new way to make AI language models, called PARSCALE, which allows them to run multiple computations at the same time using the same parameters
- Qwen团队最新论文：新的大模型扩展范式：并行扩展——Parallel Scaling Law for Language Models
当前大模型主要有两种scaling方式：堆参数（训练时扩展）和拉长推理（推理时扩展）。 前者吃显存，后者拖时延。 Qwen和浙大最新论文提出第三条路——PARSCALE并行扩展：在模型几乎不增加参数的前提下，把同一输入同…
- Paper page - Parallel Scaling Law for Language Models
Efficient Construction of Model Family through Progressive Training Using Model Expansion (2025) Please give a thumbs up to this comment if you found it helpful!
- Parallel Scaling Law for Language Models - ADS
This method, namely parallel scaling (ParScale), scales parallel computation by reusing existing parameters and can be applied to any model structure, optimization procedure, data, or task
- Parallel Scaling Law for Language Models - emergentmind. com
This paper introduces ParScale, a parallel scaling law for language models that enhances performance using concurrent processing with substantially lower memory and latency costs
- 一作解读！从idea视角，聊聊Qwen推出的新Scaling Law——Parallel Scaling
几种 Scaling 曲线的对比 Parallel Scaling Law 我们首先进行了一系列理论分析，得出一个结论： 将一个参数量为 N 的模型并行 P 个流，等价于将参数量变为原来的 倍 （详见论文的分析）。 其中 Diversity 与不同流之间的残差相关系数有关，不好接着分析。
- Parallel Scaling Law for Language Models,arXiv - CS - Machine Learning . . .
This method, namely parallel scaling (ParScale), scales parallel computation by reusing existing parameters and can be applied to any model structure, optimization procedure, data, or task
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