Google DeepMind Researchers Introduce InfAlign: A Machine Learning Framework for Inference-Aware Language Model Alignment

Generative language models face persistent challenges in moving from training to practical application. A major difficulty lies in aligning these models so that they perform optimally during inference. Current methods, such as reinforcement learning from human feedback (RLHF), focus on improving success rates compared to a reference model. However, they often overlook the role of inference-time decoding strategies such as Best-of-N sampling and controlled decoding. This mismatch between training objectives and real-world use can lead to inefficiencies, affecting the quality and reliability of results.

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 timing methods into the alignment process, aiming to bridge the gap between training and application. It does this through a calibrated reinforcement learning approach that adjusts reward functions based on specific inference strategies. InfAlign is particularly effective for techniques such as Best-of-N sampling, where multiple responses are generated and the best one is selected, and Worst-of-N, which is often used for security assessments. This approach ensures that aligned models perform well in both controlled environments and real-world scenarios.

Technical information and benefits

At the core of InfAlign is the Calibrate and Transform Reinforcement Learning (CTRL) algorithm, which follows a three-step process: calibrate reward scores, transform these scores based on inference strategies, and solve a regularized optimization problem by KL. By tailoring reward transformations to specific scenarios, InfAlign aligns training objectives with inference needs. This approach improves inference time gain rates while maintaining computational efficiency. Beyond performance metrics, InfAlign adds robustness, allowing models to handle various decoding strategies effectively and produce consistent, high-quality results.

Empirical results and insights

The effectiveness of InfAlign is demonstrated using the Anthropic Utility and Harmtaining data sets. In these experiments, InfAlign improved inference time gain rates by 8% to 12% for Best of N sampling and by 4% to 9% for Worst of N security evaluations compared to existing methods. These improvements are attributed to their calibrated reward transformations, which address poor reward model calibrations. The framework reduces absolute errors and ensures consistent performance across different inference scenarios, making it a reliable and adaptable solution.

Conclusion

InfAlign represents a significant advance in aligning generative language models for real-world applications. By incorporating inference-aware strategies, you address key discrepancies between training and implementation. Its strong theoretical foundation and empirical results highlight its potential to improve ai system alignment holistically. As generative models are increasingly used in various applications, frameworks like InfAlign will be essential to ensure both effectiveness and reliability.

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