Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic
  Thought Reward
Authors: name: Yong Deng, name: Guoqing Wang, name: Zhenzhe Ying, name: Xiaofeng Wu, name: Jinzhen Lin, name: Wenwen Xiong, name: Yuqin Dai, name: Shuo Yang, name: Zhanwei Zhang, name: Qiwen Wang, name: Yang Qin, name: Changhua Meng

Large language models (LLMs) exhibit remarkable problem-solving abilities,
but struggle with complex tasks due to static internal knowledge.
Retrieval-Augmented Generation (RAG) enhances access to external information,
yet remains limited in multi-hop reasoning and strategic search due to rigid
workflows. Recent advancements in agentic deep research empower LLMs to
autonomously reason, search, and synthesize information. However, current
approaches relying on outcome-based reinforcement learning (RL) face critical
issues such as conflicting gradients and reward sparsity, limiting performance
gains and training efficiency. To address these, we first propose Atomic
Thought, a novel LLM thinking paradigm that decomposes reasoning into
fine-grained functional units. These units are supervised by Reasoning Reward
Models (RRMs), which provide Atomic Thought Rewards (ATR) for fine-grained
guidance. Building on this, we propose Atom-Searcher, a novel RL framework for
agentic deep research that integrates Atomic Thought and ATR. Atom-Searcher
uses a curriculum-inspired reward schedule, prioritizing process-level ATR
early and transitioning to outcome rewards, accelerating convergence on
effective reasoning paths. Experiments on seven benchmarks show consistent
improvements over the state-of-the-art. Key advantages include: (1)
Atom-Searcher scales computation at test-time. (2) Atomic Thought provides
supervision anchors for RRMs, bridging deep research tasks and RRMs. (3)
Atom-Searcher exhibits more interpretable, human-like reasoning patterns.