Cycle-Consistent Search: Question Reconstructability as a Proxy Reward for Search Agent Training
Summary: arXiv:2604.12967v1 Announce Type: new
Abstract
Reinforcement Learning (RL) has shown strong potential for optimizing search agents in complex information retrieval tasks. However, existing approaches predominantly rely on gold supervision, such as ground-truth answers, which is difficult to scale. To address this limitation, we propose Cycle-Consistent Search (CCS), a gold-supervision-free framework for training search agents, inspired by cycle-consistency techniques from unsupervised machine translation and image-to-image translation.
Introduction
The field of information retrieval has been significantly enhanced by the application of Reinforcement Learning (RL). Traditional methods often depend on high-quality labeled data, which is not only expensive but also difficult to obtain in vast quantities. The Cycle-Consistent Search (CCS) framework introduces a novel approach that circumvents the need for gold supervision, thereby increasing the scalability of training search agents.
Key Hypothesis
Our key hypothesis posits that an optimal search trajectory serves as a lossless encoding of the question’s intent. Unlike insufficient or irrelevant search paths, a high-quality trajectory is capable of preserving the necessary information to accurately reconstruct the original question. This property enables the trajectory to provide a reward signal that can be utilized for policy optimization.
Challenges of Naive Cycle-Consistency Objectives
While cycle-consistency objectives offer a promising avenue for training, they are susceptible to information leakage. Specifically, reconstructing the original question might rely on superficial lexical cues instead of the substantive search process itself. This reliance can undermine the effectiveness of the training framework.
Proposed Solutions
To mitigate the risk of information leakage, we incorporate several information bottlenecks:
- Exclusion of the Final Response: This ensures that the model does not depend on the final answer, which could skew the learning process.
- Named Entity Recognition (NER) Masking: By masking certain elements of the search queries, we compel the model to focus on the structural aspects of the retrieved observations.
These constraints are designed to enhance the quality of the reward signal, ensuring it reflects informational adequacy rather than mere linguistic redundancy.
Experimental Results
We conducted a series of experiments on various question-answering benchmarks to evaluate the performance of CCS. The results indicate that CCS achieves performance levels comparable to those of supervised baselines. Furthermore, CCS outperforms prior methods that do not utilize gold supervision, demonstrating its effectiveness and robustness.
Conclusion
The findings suggest that Cycle-Consistent Search provides a scalable and efficient training paradigm for developing search agents in scenarios where gold supervision is limited or unavailable. By leveraging the principles of cycle-consistency and introducing effective information bottlenecks, CCS stands as a promising advancement in the field of reinforcement learning for information retrieval.