Generative language models face persistent challenges when transitioning from training to practical application. One significant difficulty lies in aligning these models to perform optimally during inference. Current methods, such as Reinforcement Learning from Human Feedback (RLHF), focus on improving win rates against a baseline model. However, they often overlook the role of inference-time decoding strategies like Best-of-N sampling and controlled decoding. This mismatch between training objectives and real-world usage can lead to inefficiencies, affecting the quality and reliability of the outputs.
To address these challenges, researchers at Google DeepMind and Google Research have developed InfAlign, a machine-learning framework designed to align language models with inference-aware strategies. InfAlign incorporates inference-time methods into the alignment process, aiming to bridge the gap between training and application. It does so through a calibrated reinforcement learning approach that adjusts reward functions based on specific inference strategies. InfAlign is particularly effective for techniques like Best-of-N sampling, where multiple responses are generated and the best one is selected, and Worst-of-N, which is often used for safety evaluations. This approach ensures that aligned models perform well in both controlled environments and real-world scenarios.
Technical Insights and Benefits
At the core of InfAlign is the Calibrate-and-Transform Reinforcement Learning (CTRL) algorithm, which follows a three-step process: calibrating reward scores, transforming these scores based on inference strategies, and solving a KL-regularized optimization problem. By tailoring reward transformations to specific scenarios, InfAlign aligns training objectives with inference needs. This approach enhances inference-time win rates while maintaining computational efficiency. Beyond performance metrics, InfAlign adds robustness, enabling models to handle diverse decoding strategies effectively and produce consistent, high-quality outputs.
Empirical Results and Insights
The effectiveness of InfAlign is demonstrated using the Anthropic Helpfulness and Harmlessness datasets. In these experiments, InfAlign improved inference-time win rates by 8-12% for Best-of-N sampling and by 4-9% for Worst-of-N safety assessments compared to existing methods. These improvements are attributed to its calibrated reward transformations, which address reward model miscalibrations. The framework reduces absolute errors and ensures consistent performance across varying inference scenarios, making it a reliable and adaptable solution.
Conclusion
InfAlign represents a significant advancement in aligning generative language models for real-world applications. By incorporating inference-aware strategies, it addresses key discrepancies between training and deployment. Its robust theoretical foundation and empirical results highlight its potential to improve AI system alignment comprehensively. As generative models are increasingly used in diverse applications, frameworks like InfAlign will be essential for ensuring both effectiveness and reliability.
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