A^2Search: Ambiguity-Aware Question Answering with Reinforcement
  Learning
Authors: name: Fengji Zhang, name: Xinyao Niu, name: Chengyang Ying, name: Guancheng Lin, name: Zhongkai Hao, name: Zhou Fan, name: Chengen Huang, name: Jacky Keung, name: Bei Chen, name: Junyang Lin
Organization: City University of Hong Kong

Recent advances in Large Language Models (LLMs) and Reinforcement Learning
(RL) have led to strong performance in open-domain question answering (QA).
However, existing models still struggle with questions that admit multiple
valid answers. Standard QA benchmarks, which typically assume a single gold
answer, overlook this reality and thus produce inappropriate training signals.
Existing attempts to handle ambiguity often rely on costly manual annotation,
which is difficult to scale to multi-hop datasets such as HotpotQA and MuSiQue.
In this paper, we present A^2Search, an annotation-free, end-to-end training
framework to recognize and handle ambiguity. At its core is an automated
pipeline that detects ambiguous questions and gathers alternative answers via
trajectory sampling and evidence verification. The model is then optimized with
RL using a carefully designed AnsF1 reward, which naturally
accommodates multiple answers. Experiments on eight open-domain QA benchmarks
demonstrate that A^2Search achieves new state-of-the-art performance. With
only a single rollout, A^2Search-7B yields an average AnsF1@1
score of 48.4% across four multi-hop benchmarks, outperforming all strong
baselines, including the substantially larger ReSearch-32B (46.2%).
Extensive analyses further show that A^2Search resolves ambiguity and
generalizes across benchmarks, highlighting that embracing ambiguity is
essential for building more reliable QA systems. Our code, data, and model
weights can be found at https://github.com/zfj1998/A2Search