AI 中文总结
研究针对智能体RAG系统中全零展开组问题，提出EviBack方法，通过证据约束教师退避提供辅助监督。利用全自动管道生成两阶段教师，提升了下游F1等指标，在多个问答基准上取得更好效果。
AI 中文摘要
强化学习使智能体RAG系统能够从可验证的结果奖励中学习多轮搜索，但全零的展开组没有比较信号，可能会隐藏有用的搜索行为。我们提出了EviBack，一种证据约束的教师退避方法，在保留可验证的智能体奖励的同时，为这些组提供辅助监督。它将证据评估与答案细化分开，防止参考答案推翻证据不足的判断。一个全自动的、端到端的GPT-5.5辅助APE管道从手动编写的单提示双任务教师开始，自动划分和标记展开数据，并进行消融、任务分解、评估和选择，以生成一个门控的两阶段教师。与手动设计相比，生成的教师提高了下游F1和有效答案率，同时减少了搜索、重复查询和强制终止。在七个开放域问答基准和三个Qwen3规模上，EviBack比Search-R1提高了F1，并提高了单跳和多跳宏F1。我们保证代码将在后期公开。
英文摘要
Reinforcement learning enables Agentic RAG systems to learn multi-turn search from verifiable outcome rewards, but all- zero rollout groups provide no comparative signal and may hide useful search behavior. We present EviBack, an evidence- constrained Teacher backoff that supplies auxiliary super- vision to such groups while preserving verifiable Actor re- wards. It separates evidence assessment from answer refine- ment, preventing reference answers from overriding evidence- insufficiency judgments. A fully automated, end-to-end GPT- 5.5-assisted APE pipeline starts from a manually authored single-prompt dual-task Teacher, automatically partitions and labels rollout data, and performs ablation, task decomposition, evaluation, and selection to produce a gated two-stage Teacher. Compared with the manual design, the resulting Teacher im- proves downstream F1 and valid-answer rate while reduc- ing search, duplicate queries, and forced termination. Across seven open-domain QA benchmarks and three Qwen3 scales, EviBack improves F1 over Search-R1 and raises both single- and multi-hop macro F1. We guarantee that the code will be made publicly available at a later stage.
