arXiv:2605.27140v1 Announce Type: new Abstract: Reinforcement learning for multi-turn agents suffers from a credit-assignment mismatch: rewards are sparse and trajectory-level, while success often hinges on a few local decisions. Existing online policy distillation (OPD) provides denser token-level supervision, but typically treats heterogeneous agent trajectories as monolithic strings rather than causal interaction units. We present StepOPSD, a post-rollout preference self-distillation framework that takes the agent step as the unit of credit redistribution. StepOPSD decomposes trajectories i
The paper addresses a core challenge in multi-turn AI agents, credit assignment, which is a significant bottleneck in their development and broader application.
Improved credit assignment in reinforcement learning directly enhances the efficiency and effectiveness of multi-turn AI agents, accelerating their sophistication and deployment.
This research provides a more granular and efficient method for training AI agents, moving beyond monolithic trajectory analysis to step-level credit redistribution.
- · AI agent developers
- · Companies deploying complex conversational AI
- · Reinforcement learning researchers
- · Legacy online policy distillation methods
More robust and capable AI agents will emerge in various applications.
The development cycle for agentic systems will shorten, leading to faster innovation in AI applications.
Complex, multi-step tasks currently requiring human intervention could be increasingly automated by advanced AI agents.
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Read at arXiv cs.AI