Morning Edition · Friday, July 10, 2026Published at 1:31 AM EDT · New York

DeepSearch-World Trains Tool-Use Agents to Improve From Their Own Experience

The method uses self-distillation inside a verifiable environment to escape the reliance on fixed teacher trajectories and sparse reinforcement learning (RL) rewards.

A new arXiv paper, DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment, targets a core bottleneck in training tool-use agents. Supervised fine-tuning relies on fixed trajectories distilled from a teacher m…