RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents
Multi-turn tool-use RL is bottlenecked by the rapid depletion of informative samples in static datasets. We observe that the gradient signal in GRPO concentrates on tasks with the highest rollout reward variance, a consequence of the Popoviciu upper bound. Consequently, samples near the agent's capability boundary -- where successes and failures are roughly balanced -- contribute disproportionately large policy gradients. As training progresses, this boundary continuously shifts, which gradually depletes the pool of informative samples in a static dataset. We propose RODS (Reward-driven Online Data Synthesis) to resolve this depletion. RODS closes the loop between RL training and data gen...
| Authors | Ruishan Fang, Siyuan Lu, Chenyi Zhuang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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