Google DeepMind Unveils InfAlign for Language Model Inference

Introduction

Generative language models are increasingly utilized in various applications, yet they face challenges in transitioning from training to practical deployment. A key hurdle is optimizing model performance during the inference phase. In light of this, Google DeepMind has introduced a novel framework called InfAlign, designed to enhance the alignment capabilities of language model inference.

The Challenge of Inference

Current methodologies, such as reinforcement learning from human feedback (RLHF), primarily focus on boosting a model's success rate. However, they often overlook essential decoding strategies during inference, including techniques like Best-of-N sampling and controlled decoding. This disconnect can lead to inefficiencies and suboptimal output quality, highlighting the need for improved alignment between training objectives and practical usage.

Introducing InfAlign

To tackle these challenges, the collaborative effort between Google DeepMind and Google Research led to the development of InfAlign. This machine learning framework integrates crucial methods during inference into the alignment process, effectively bridging the gap between the training phase and real-world application. InfAlign accomplishes this by adjusting reward functions based on specific inference strategies, employing a calibrated reinforcement learning approach. Notably, it enhances the performance of models using techniques like Best-of-N sampling, which generates multiple responses to select the best, and Worst-of-N sampling, typically utilized for safety evaluations.

The Core Algorithm: CTRL

At the heart of InfAlign lies the Calibrated and Transformed Reinforcement Learning (CTRL) algorithm. This algorithm operates through a three-step process:

1. Calibrating reward scores
2. Transforming these scores according to the chosen inference strategy
3. Solving a KL regularization optimization problem By customizing reward transformations for specific scenarios, InfAlign aligns training objectives with inference needs. This not only boosts success rates during inference but also maintains computational efficiency. Furthermore, it enhances the model's robustness, enabling it to effectively manage various decoding strategies and consistently deliver high-quality outputs.

Experimental Validation

InfAlign's effectiveness was validated through experiments utilizing Anthropic's usefulness and harmlessness datasets. The results demonstrated that InfAlign improved the inference success rate in Best-of-N sampling by 8%-12% and in Worst-of-N evaluations by 4%-9% compared to existing methods. These enhancements stem from InfAlign’s calibrated reward transformations, which address previous miscalibration issues and ensure reliable performance across different inference scenarios.

Conclusion

InfAlign marks a significant advancement in aligning generative language models. By integrating inference-aware strategies, it effectively addresses the critical discrepancies between model training and deployment. With a solid theoretical foundation and empirical support, InfAlign has the potential to substantially improve the alignment of AI systems.

For more technical details, refer to the study available at: arXiv

Key Points

1. InfAlign is a new framework developed by Google DeepMind aimed at enhancing the performance of language models during the inference phase.
2. This framework aligns training objectives with inference needs by adjusting reward functions for inference strategies through calibrated reinforcement learning methods.
3. Experimental results indicate that InfAlign significantly improves the inference success rate of models across multiple tasks, demonstrating good adaptability and reliability.