Heterogeneous Co-Evolution Learning for Tool-Efficient Search Agents with Zero Labels
arXiv:2610.39682v1 Announce Type: cross Abstract: Deep search agents extend language models by interleaving reasoning with external retrieval. However, effective search-agent training requires addressing two challenges beyond final-answer optimization. First, the capability frontier of agents is largely determined by training questions, whereas fixed or labeled task collections cannot adapt as agents improve. Second, outcome-only reinforcement learning treats all successful trajectories equally…