Traverse: Learning When to Remember, Reset, and Redirect for Long-Horizon Web Search

The paper introduces Traverse, an autonomous web‑search agent that manages its search process through three states—Rubric, Answer, and Verify—while using a Seal Memory tool for active context management. Reinforcement learning is employed to train the agent, but a training instability called Seal Collapse is mitigated by training only the final segment after context management. The resulting 35B model achieves state‑of‑the‑art performance on BrowseComp and related benchmarks, outperforming comparable open‑source systems.

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• agents
• reinforcement-learning