Synthetic personas support LLM personalization, social simulation, and alignment evaluation, yet existing resources typically represent each individual with only a small number of structured attributes. We introduce DeepPersona, a generative engine that scales persona depth to roughly 200 instantiated taxonomy nodes while preserving narrative coherence. DeepPersona combines (i) a hierarchical taxonomy of 8,496 human-descriptive attributes mined from 62k personalizable QA pairs in real user-chatbot dialogues and (ii) a progressive sampler that conditions each new attribute on the evolving persona and uses a stratified similarity prior to balance coherence and diversity. The same engine can expand shallow inputs, such as PersonaHub entries, into deep profiles. Across three complementary evaluations, DeepPersona improves judge-extracted attribute count by 32% and judge-rated uniqueness by 44% over the strongest prior persona resource; improves LLM personalization quality by up to 11.6% across ten dimensions, with human evaluators preferring DeepPersona responses in 81.2% to 87.0% of pairwise comparisons; and reduces the distributional gap to real World Values Survey responses by 31.7%, while recovering Big Five personality distributions 17% closer to ground truth than the strongest LLM baseline.

Large Language Models (LLMs) with web search capabilities show significant potential for deep research, yet current methods—brittle prompt engineering or RAG-based reinforcement learning in controlled environments—fail to capture real-world complexities. In this paper, we introduce DeepResearcher, the first comprehensive framework for end-to-end training of LLM-based deep research agents through scaling reinforcement learning (RL) in real-world environments with authentic web search interactions. Unlike RAG approaches reliant on fixed corpora, DeepResearcher trains agents to navigate the noisy, dynamic open web. We implement a specialized multi-agent architecture where browsing agents extract relevant information from various webpage structures and overcoming significant technical challenges. Extensive experiments on open-domain research tasks demonstrate that DeepResearcher achieves substantial improvements of up to 28.9 points over prompt engineering-based baselines and up to 7.2 points over RAG-based RL agents. Our qualitative analysis reveals emergent cognitive behaviors from end-to-end RL training, such as planning, cross-validation, self-reflection for research redirection, and maintain honesty when unable to find definitive answers. Our results highlight that end-to-end training in real-world web environments is fundamental for developing robust research capabilities aligned with real-world applications. The source codefor DeepResearcher is released at: https://github.com/GAIR-NLP/DeepResearcher.