Jul 10, 2026, 4:00 AM

DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment

TickrWire Editorial Desk·Jul 10, 2026, 4:00 AM·1 min read AI-assisted, human-reviewed

Reported by arXiv cs.CL: DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment. Analysis and context written by TickrWire.

30-second summary

arXiv:2607.07820v1 Announce Type: new Abstract: Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks constructed from entity-level random walks and sup

TickrWire
Full story

arXiv:2607.07820v1 Announce Type: new

Abstract: Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks constructed from entity-level random walks and sup

Sources · 1
More stories