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Results show that long-horizon reflective data is an effective route toward self-improving agents, and synthesize long-horizon improvement trajectories from machine learning and algorithmic programming tasks, two domains that offer verifiable feedback and reward sustained iteration.
Results validate the effectiveness of combining autonomous, socially grounded data governance with preference-aligned training in practical search systems and demonstrate significant improvements in both offline relevance metrics and online user engagement indicators, along with enhanced annotation efficiency.
This work introduces AREX, a family of Recursively Self-Improving (RSI) deep research agents that substantially outperforms comparable-scale baselines and remains competitive with models using substantially more activated parameters.
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