CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning
Summary: arXiv:2601.05858v2 Announce Type: replace-cross
Introduction
In the realm of artificial intelligence, large language models (LLMs) have revolutionized the field of machine translation (MT), particularly in zero-shot multilingual contexts. While significant progress has been made, particularly through preference optimization techniques, a critical factor remains underexplored: the sequence in which training data is presented to the model. This article introduces a new approach to enhancing MT performance through a novel curriculum learning strategy known as CLewR (Curriculum Learning with Restarts).
The Importance of Curriculum Learning
Curriculum learning, inspired by the educational principle of presenting information in a structured manner, plays a crucial role in the training of AI models. By carefully organizing data samples from easy to difficult, models can learn more effectively. Traditional methods, however, often overlook the need for reiteration, which is vital for reinforcing the learning of simpler concepts.
Introducing CLewR
CLewR introduces an innovative curriculum learning strategy that incorporates restarts. This method allows for multiple iterations of an easy-to-hard curriculum throughout the training process. By frequently revisiting simpler examples, CLewR effectively mitigates the phenomenon known as catastrophic forgetting, where models tend to lose previously acquired knowledge as they learn new information.
Methodology
The implementation of CLewR involves the following key components:
- Data Structuring: Data samples are organized based on their complexity, ensuring a logical progression from simpler to more complex tasks.
- Reiteration Process: The easy-to-hard curriculum is repeated multiple times during training, allowing for reinforcement of foundational knowledge.
- Integration with Preference Optimization: CLewR can be seamlessly integrated with various state-of-the-art preference optimization algorithms, enhancing their effectiveness.
Results
The implementation of CLewR has shown promising results across several model families, including:
- Gemma2
- Qwen2.5
- Llama3.1
Experimental results indicate that models utilizing the CLewR strategy consistently outperform those trained without it, demonstrating significant improvements in translation accuracy and overall performance.
Conclusion
The integration of CLewR into machine translation systems highlights the importance of data presentation order in training models. By emphasizing a structured approach to learning, CLewR not only improves performance but also addresses the critical issue of forgetting in AI models. Researchers and practitioners are encouraged to explore this methodology further, with the code publicly available at https://github.com/alexandra-dragomir/CLewR.
Future Directions
As the field of machine translation continues to evolve, further research into curriculum learning strategies will be essential. The insights gained from CLewR may pave the way for new approaches that enhance the learning capabilities of AI models, ultimately leading to more robust and efficient systems.