Keep Policy Gradient in Charge: Sibling-Guided Credit Distillation for Long-Horizon Tool-Use Agents
arXiv:2606.12634v1 Announce Type: cross Abstract: Long-horizon tool-use reinforcement learning can learn from outcome verification, but its trajectory-level advantage is broadcast across many reasoning, API, and answer tokens. Self-distillation promises a denser signal by reusing a policy's own rollouts or a privileged teacher. We show, however, that direct token-level self-distillation can silently destroy tool use: it rehearses teacher behavior without knowing which actions the verifier rewards, so useful skills and harmful shortcuts are amplified together. We introduce Sibling-Guided Credit
This research addresses a critical challenge in developing robust long-horizon AI agents, which is paramount for practical applications as AI capabilities advance.
Improving credit assignment in long-horizon tasks is essential for the reliable and scalable development of autonomous AI agents, impacting their deployment across various industries.
The proposed 'Sibling-Guided Credit Distillation' method offers a more stable and effective way to train tool-use agents, potentially accelerating their reliability and widespread adoption.
- · AI software developers
- · Automation industries
- · AI agent providers
- · Companies relying on brittle AI systems
More capable and trustworthy autonomous AI agents become available for complex tasks.
Increased adoption of AI agents leads to automation of more sophisticated workflows.
The definition of white-collar work shifts significantly as agents take on increasingly complex, multi-step Reasoning tasks, altering labor markets.
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Read at arXiv cs.CL