QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search (2502.02584v1)

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.

• The paper introduces a Q-guided stepwise search framework that significantly improves language agent inference via intermediate Q-value rewards.
• It leverages an exploration tree and a QNet, applying the Bellman equation to effectively assess action quality at each decision point.
• Empirical tests across benchmarks show that QLASS outperforms both open-source and proprietary models, even with limited annotated data.

Analyzing the Novel Q-Guided Language Agent Stepwise Search (QLASS)

The methodology articulated in the paper titled "QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search" presents a compelling framework to address the inherent challenges faced by language-based agents in complex interactive scenarios. The research emphasizes the importance of stepwise guidance in enhancing the efficacy of these agents, demonstrating significant improvements in inference by employing a strategy grounded in Q-value estimation.

The LLMs deployed today, while exhibiting remarkable capabilities in various domains, often struggle with complex agentic tasks due to their reliance on outcome-based reward systems. Such models excel in generating sequences or actions based on learned data, yet without effective intermediary evaluation, they falter in optimizing long-term value during agent interaction. The traditional reliance on outcome rewards can result in sub-optimal policies, as these rewards only provide feedback at the end of a trajectory without assessing the quality of decisions along the way.

QLASS introduces an innovative process that employs Q-values as stepwise rewards through Q-guided Language Agent Stepwise Search. A central element of QLASS is its exploration tree mechanism, which structures and evaluates trajectories by estimating the expected future rewards (Q-values) for each action taken. This approach leverages the BeLLMan equation to formalize and compute these values, effectively embedding the idea of future utility at each decision point. These Q-values are used to train a QNet, which acts as a powerful process reward model capable of giving nuanced intermediate feedback to the language agents.

The stepwise guidance offered by the QNet facilitates a Q-guided generation strategy. During inference, the language agent samples possible actions, each evaluated by the QNet to allow the selection of those with the highest estimated returns. This enables the language agent to adapt more adeptly to scenarios requiring understanding of long-term consequences, thus fundamentally improving the decision-making process at each step. The strategic focus on process rewards, rather than relying solely on outcomes, addresses the misalignments between achieved objectives and the optimality of actions along complex interaction paths.

Empirical results substantiate the claims, with QLASS outperforming several baseline models across different benchmarks like WebShop, ALFWorld, and SciWorld. These environments, each presenting distinct challenges, serve as rigorous testing grounds for evaluating the efficacy of language agents in complex, interactive tasks. QLASS demonstrated superior performance over both open-sourced and proprietary closed-source models, maintaining effectiveness even when subjected to the constraint of reduced annotated datasets—an advantage that highlights its prowess in mitigating the dependency on extensive human supervision for model training.

Additionally, the paper explores various aspects of process reward modeling by comparing QLASS's Q-value guided strategy against other step-level evaluation models, such as those utilizing average rewards. The results decisively favor the Q-value approach, underscoring its robustness and reaffirming the proposition that stepwise Q-value guidance can yield enhanced decision-making for language agents.

The theoretical and practical implications of this research are profound. By shifting focus from outcomes to nuanced stepwise evaluations, this work opens up avenues for developing more adaptive, cognitive agents that can navigate complex environments with increased autonomy and intelligence. Future advancements can further refine such models, potentially integrating them with real-time feedback loops and adaptive learning paradigms, thereby extending the frontier of cognitive AI agents capable of sophisticated interaction across a variety of domains.