QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search

TL;DR

QLASS enhances language agent inference by estimating Q-values stepwise, providing intermediate guidance that improves decision-making and performance, especially with limited annotated data.

Contribution

Introducing QLASS, a method that generates intermediate Q-value annotations for language agents, improving long-term decision making and efficiency with less supervision.

Findings

Significant performance gains on complex tasks.

Effective with nearly half the annotated data.

More accurate decision making demonstrated qualitatively.

Abstract

Language agents have become a promising solution to complex interactive tasks. One of the key ingredients to the success of language agents is the reward model on the trajectory of the agentic workflow, which provides valuable guidance during training or inference. However, due to the lack of annotations of intermediate interactions, most existing works use an outcome reward model to optimize policies across entire trajectories. This may lead to sub-optimal policies and hinder the overall performance. To address this, we propose QLASS (Q-guided Language Agent Stepwise Search), to automatically generate annotations by estimating Q-values in a stepwise manner for open language agents. By introducing a reasoning tree and performing process reward modeling, QLASS provides effective intermediate guidance for each step. With the stepwise guidance, we propose a Q-guided generation strategy to enable language agents to better adapt to long-term value, resulting in significant performance improvement during model inference on complex interactive agent tasks. Notably, even with almost half the annotated data, QLASS retains strong performance, demonstrating its efficiency in handling limited supervision. We also empirically demonstrate that QLASS can lead to more effective decision making through qualitative analysis. We will release our code and data.