Generative language models face persistent challenges when moving from training to real-world applications. One significant difficulty lies in tuning these models for optimal performance during inference. Current techniques, such as reinforcement learning from human feedback (RLHF), focus on improving the win rate over baseline models. However, the role of decoding strategies during inference, such as best-of-N sampling and controlled decoding, is often overlooked. This mismatch between training objectives and actual usage can create inefficiencies and impact the quality and reliability of the output.
To address these challenges, researchers at Google DeepMind and Google Research developed InfAlign, a machine learning framework designed to align language models with inferential recognition strategies. InfAlign incorporates inference-time methods into the alignment process, aiming to bridge the gap between training and application. This is done through a tailored reinforcement learning approach that adjusts the reward function based on a specific inference strategy. InfAlign is particularly effective for techniques such as Best-of-N sampling, where multiple responses are generated and the best response is selected, and Worst-of-N, which is often used for safety evaluation. This approach ensures that the calibrated model behaves 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. The algorithm follows a three-step process: adjusting reward scores, transforming these scores based on an inference strategy, and solving a KL regularization optimization problem. InfAlign aligns training goals with inference needs by tailoring reward transformations to specific scenarios. This approach improves the win rate during inference while maintaining computational efficiency. InfAlign adds robustness on top of performance metrics, allowing models to effectively handle diverse decoding strategies and produce consistent, high-quality output.
Empirical results and insights
The effectiveness of InfAlign is demonstrated using the human usefulness and benignity dataset. In these experiments, InfAlign improves the inference time win rate by 8-12% for best-of-N sampling and by 4-9% for worst-of-N safety evaluation compared to existing methods. did. These improvements are due to adjusted reward transformations that address miscalibrations in the reward model. This framework reduces absolute errors and guarantees consistent performance across different inference scenarios, making it a reliable and adaptable solution.
conclusion
InfAlign represents a significant advance in tuning generative language models for real-world applications. Incorporating an inference-aware strategy addresses the key mismatch between training and deployment. Its robust theoretical foundation and empirical results highlight its potential to comprehensively improve the coordination of AI systems. Generative models are increasingly used in a wide variety of applications, and frameworks like InfAlign are essential to ensuring both validity and reliability.
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