Iterative Value Function Optimization for Guided Decoding
Zhenhua Liu, Lijun Li, Ruizhe Chen, Yuxian Jiang, Tong Zhu, Wenliang Chen, Jing Shao
2025-03-05
Summary
This paper talks about a new way to make AI language models better at generating text that matches what humans want, called Iterative Value Function Optimization (IVO). It's designed to be more efficient and stable than current methods.
What's the problem?
Current methods for improving AI text generation, like Reinforcement Learning from Human Feedback (RLHF), are expensive and unstable. Other methods that try to guide the AI's decisions during text generation often struggle to accurately predict which choices will lead to the best outcomes.
What's the solution?
The researchers created IVO, which has two main parts. First, it uses Monte Carlo Value Estimation to explore many different ways the AI could generate text, helping it understand which choices are best. Second, it uses Iterative On-Policy Optimization to keep improving its understanding of good choices by learning from its own successful attempts. This helps the AI get better at generating text that humans like without needing to completely retrain the whole model.
Why it matters?
This matters because it could make it easier and cheaper to improve AI language models. By using IVO, developers could create AI that's better at tasks like summarizing text, having conversations, and following instructions, without needing as much computing power. This could lead to more useful and responsive AI assistants that are better aligned with what humans actually want.
Abstract
While Reinforcement Learning from Human Feedback (RLHF) has become the predominant method for controlling language model outputs, it suffers from high computational costs and training instability. Guided decoding, especially value-guided methods, offers a cost-effective alternative by controlling outputs without re-training models. However, the accuracy of the value function is crucial for value-guided decoding, as inaccuracies can lead to suboptimal decision-making and degraded performance. Existing methods struggle with accurately estimating the optimal value function, leading to less effective control. We propose Iterative Value Function Optimization, a novel framework that addresses these limitations through two key components: Monte Carlo Value Estimation, which reduces estimation variance by exploring diverse trajectories, and Iterative On-Policy Optimization, which progressively improves value estimation through collecting trajectories from value-guided policies. Extensive experiments on text summarization, multi-turn dialogue, and instruction following demonstrate the effectiveness of value-guided decoding approaches in aligning language models. These approaches not only achieve alignment but also significantly reduce computational costs by leveraging principled value function optimization for efficient and effective control.