Self-Evolving Deep Research via Joint Generation and Evaluation
Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capability. Unlike traditional question-answering (QA) tasks, deep research report generation lacks definitive ground-truth, making reward design inherently unverifiable and limiting effective reinforcement learning. Existing approaches mitigate this challenge with LLM-as-a-judge and query-dependent evaluation rubrics, but they still rely on static evaluators that cannot adapt their standards as the solver improves, leading to insufficient and eventually saturated optimization pressure. We address this limitation with a \textbf{s}elf-evolving \te...
| Authors | Han Zhu, Chengkun Cai, Yuanfeng Song et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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