Deepresearch (DR) agents interact with real-world web environments through multi-turn search and visit, causing their contexts to grow rapidly over time.
We observe that, even after DR Agentic Reinforcement Learning (DR-RL), 61.6% of the model's remaining prediction errors can still be attributed to insufficient long-context understanding, including long-context hallucination and failures in cross-document evidence integration.
It motivates us to further break the bottleneck of DR-RL by strengthening the model's long-context ability.
However, effective LongContext training requires more than simply increasing context length.
To bridge the data gap, we propose `DR Rollouts to LongContext-QA (DR-to-Long)'.
The method repurposes DR-RL trajectories, which naturally contain search histories, visited webpages, evidence snippets, and final-answer supervision.
It then replaces the compact snippets and webpage summaries in each trajectory with the full contents of their corresponding URLs, producing substantially longer multi-document contexts while preserving the original evidence relationships.
Building on DR-to-Long, we introduce DLD (DR -> LongQA -> DR)-RL.
DLD-RL first performs a short DR-RL stage to collect rollout trajectories, which are then converted into LongQA instances at zero annotation cost.
The model is subsequently optimized with LongQA-RL to strengthen LongContext ability, followed by full DR-RL to continue improving its DR capability.
Experiments show that DLD-RL outperforms standard DR-RL by 7.3% on three Deepresearch benchmarks and improves performance by 13.5% on three long-context benchmarks.