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Paper TitleARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from Transformer
Publication DateJanuary 2025
AuthorsLin Yueyu, Li Zhiyuan, Peter Yue, Liu Xiao
AffiliationUnknown
Original Linkhttps://arxiv.org/pdf/2501.15570
Open Source Code Linkhttps://github.com/yynil/RWKVInside
Demo Linkhttps://huggingface.co/RWKV-Red-Team/ARWKV-7B-Preview-0.1
Introduction
In recent years, with the rapid development of linear RNNs, new model architectures such as RWKV-7 have shown strong competitiveness. However, the limitations of traditional Transformer models in long-context learning remain a bottleneck. This paper introduces a new language model based on RNN and Attention, aiming to enhance the expressiveness of RNNs while achieving more efficient knowledge processing without sacrificing performance.
Background and Related Work
In the field of natural language processing, the Transformer model has always been dominant, but its performance in long-context learning is not ideal. In recent years, researchers have begun to explore new architectures that combine RNNs (Recurrent Neural Networks) with Attention mechanisms, aiming to retain the advantages of Transformers while enhancing model expressiveness. The RWKV series models are among the best in this regard. RWKV-7 introduces a novel structure that, through the incorporation of a Time Mixing Module, achieves performance that surpasses traditional Transformers with fewer parameters.
Method Overview
This study proposes a novel language model ARWKV based on RNN and Attention, which implements a replacement for the self-attention mechanism within the time mixing module. By employing knowledge distillation techniques, the researchers successfully transferred knowledge from large language models to smaller models while maintaining performance. This method not only enhances the model’s expressiveness but also significantly shortens training time.
Core Design
The core of the ARWKV model lies in the design of its time mixing module. This module provides more efficient state tracking capabilities by replacing the traditional self-attention mechanism. By adjusting the model’s structure and introducing a new distillation process, researchers successfully reduced computational complexity and resource requirements while maintaining performance. This enables ARWKV to demonstrate strong competitiveness in multiple benchmark tests.
Main Ideas of the Paper
The main idea of this study is to enhance the expressiveness of RNNs in natural language processing tasks through the combination of distillation and the time mixing module. Below is a summary of key information from the paper:
*The table can be scrolled horizontally if it exceeds the view
| Application Scenario | Model Backbone and Reason for Selection | Loss Function | Training Dataset | Testing Dataset | Training Method | Experimental Results | Advantages of the Method | Disadvantages of the Method |
|---|---|---|---|---|---|---|---|---|
| Natural Language Processing Tasks | RWKV-7, due to its efficient time mixing module | Cross-entropy loss | 20M tokens | Various benchmark datasets | Knowledge distillation | Comprehensive improvement over SOTA | Efficient, low resource demand | Increased complexity |
Experimental Results
In experiments, the ARWKV model demonstrated its performance across multiple benchmark tests. Compared to models like Qwen2.5-7B-Instruct, ARWKV showed outstanding performance in various tasks.
*The table can be scrolled horizontally if it exceeds the view
| Task | Qwen2.5-7B-Instruct | ARWKV |
|---|---|---|
| MMLU | 71.72 | 64.77 |
| Squad | 47.89 | 40.35 |
| WinoGrande | 71.35 | 68.98 |
| GSM8K | 82.34 | 47.99 |
Despite ARWKV’s slightly underwhelming performance in certain tasks, it has significant advantages in resource consumption and training efficiency. Especially during the knowledge distillation process of large-scale models, ARWKV has shown strong potential.
Conclusion and Future Outlook
The ARWKV model introduced in this paper demonstrates an innovative combination of RNN and Attention mechanisms, successfully transferring knowledge from large models to smaller models through efficient knowledge distillation methods. In the future, with further optimization and improvements of ARWKV, its applications in multimodal architectures and hybrid architectures will receive more validation and promotion. This not only enhances the model’s reasoning capabilities but also provides new ideas and directions for different computational paradigms.
Long Ge’s Review
Innovation Score of the Paper:★★★★☆
The innovation of this paper in combining RNN and Attention mechanisms is noteworthy, especially through knowledge distillation to enhance the expressiveness of smaller models.
Reasonableness of Experiments:★★★☆☆
The experimental setup is somewhat simplistic, and the fairness of certain comparative experiments needs further consideration.
Academic Research Value:★★★★☆
Theoretically, this method provides a new perspective for the application of RNNs, possessing high academic research value.
Stability:★★★☆☆
The current method’s stability and robustness require more experimental proof, especially in different application scenarios.
Adaptability and Generalization Ability:★★★☆☆
The model performs excellently in certain specific tasks, but its adaptability and generalization ability still need further enhancement.
Hardware Requirements and Costs:★★★★☆
Compared to some large models, ARWKV has certain advantages in resource consumption, but further optimization is needed to reduce computational costs.
Potential Issues: The model performs poorly on certain tasks, which may be related to the design of the attention mechanism; future experiments are needed for verification and improvement.
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