SOURCE-LINKED INTELLIGENCE

UnifiedPlayers: Enhance Tool-Integrated Reasoning in Agentic Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Self-evolving methods reduce the need for human-annotated trajectories by allowing tool-using agents to generate their own training data. Yet existing methods typically separate trajectory generation from evaluation, relying on static verifiers that cannot adapt to emerging failure modes or self-consistency signals that may reinforce errors shared across trajectories. Jointly adapting planning, execution, and evaluation offers a promising alternative, but introduces a fundamental coordination challenge: each component continuously changes the data or feedback used to train the others. We addre

Read original source ↗ Open in workspace

recordType

paper

region

Global