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EvoSyn is proposed, a learning-feedback-driven multi-agent framework that organizes synthesis around a revisable state specifying what interaction factors can be deliberately generated and where synthesis effort should be allocated.
Privileged Self-Practice (PSP), which keeps the PI and moves it from the loss to the sampler and is the only method that consistently outperforms plain GRPO, improving task-goal completion on AppWorld and the resolved rate by up to 61% on SWE-bench Verified.
This work proposes a closed-loop Multi-Agent Simulation Framework to synthe-size diverse, faithful, and policy-aligned shopping trajectories, and presents synthetic data that enables a small model to significantly outperform same-size baselines and surpass a large-model baseline.
Across four RoboTwin tasks spanning different horizons and coordination patterns, Prism-GRPO improves success and quality at matched rollout budgets and reaches target success rates with up to 56% fewer rollouts.
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