Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and
  Synthesis for SLMs
Authors: name: Shreyas Singh, name: Kunal Singh, name: Pradeep Moturi
Organization: Fractal AI Research

Tool-integrated reasoning has emerged as a key focus for enabling agentic
applications. Among these, DeepResearch Agents have gained significant
attention for their strong performance on complex, open-ended
information-seeking tasks. We introduce Fathom-DeepResearch, an agentic system
composed of two specialized models. The first is Fathom-Search-4B, a DeepSearch
model trained from Qwen3-4B and optimized for evidence-based investigation
through live web search and targeted webpage querying. Its training combines
three advances: (i) DUETQA, a 5K-sample dataset generated via multi-agent
self-play that enforces strict web-search dependence and heterogeneous source
grounding; (ii) RAPO, a zero-overhead extension of GRPO that stabilizes
multi-turn Reinforcement Learning with Verifiable Rewards through curriculum
pruning, reward-aware advantage scaling, and per-prompt replay buffers; and
(iii) a steerable step-level reward that classifies each tool call by cognitive
behavior and marginal utility, enabling explicit control over search trajectory
breadth, depth, and horizon. These improvements enable reliable extension of
tool-calling beyond 20 calls when warranted. The second is
Fathom-Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn
DeepSearch traces into structured, citation-dense DeepResearch Reports for
comprehensive synthesis. Evaluated on DeepSearch benchmarks (SimpleQA, FRAMES,
WebWalker, Seal0, MuSiQue) and DeepResearch-Bench, the system achieves
state-of-the-art performance in the open-weights category while demonstrating
strong generalization to diverse reasoning tasks including HLE, AIME-25,
GPQA-Diamond, and MedQA.