arXiv:2606.19047v1 Announce Type: new Abstract: 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 stati
This research addresses a fundamental bottleneck in multi-turn tool-use RL, highlighting a critical limitation as AI agents become more complex and autonomous.
Improving data efficiency and learning robustness in multi-turn tool-use agents is crucial for scaling agentic systems and expanding their capabilities across various domains.
The proposed method (RODS) enables more efficient and continuous learning for AI agents by dynamically generating informative samples, potentially accelerating the development of advanced agent behaviors.
- · AI agent developers
- · Companies deploying autonomous AI systems
- · Generative AI platforms
- · AI development relying solely on static datasets
More robust and generalizable AI agents emerge capable of handling complex, multi-step tasks.
Accelerated deployment of AI agents in white-collar workflows, automating previously human-centric processes.
Enhanced AI agent capabilities could lead to new forms of human-AI collaboration and economic disruption.
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Read at arXiv cs.AI