A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data for Agent Post-Training

arXiv·medium signal

'A Few Teacher Steps Go a Long Way' (arXiv:2607.04574, July 6, 2026) reframes agent post-training data as a budget-allocation problem: instead of cloning full teacher demonstrations (which mismatch the contexts a student actually hits at test time), spend a fixed teacher-labeling budget on short 'continuation' rollouts that branch from the student's own trajectories. On HotpotQA, ALFWorld, and Terminal-Bench-Dev, bounded teacher continuations beat pure behavioral cloning and success-filtering at matched budgets. For builders fine-tuning agents: generate a few teacher-corrected continuations from your student's failure states rather than paying for many complete expert trajectories.