# CAMG-II

Long-horizon agents accumulate interaction histories that outgrow the active context, so they must learn what to write down and when to read it back. Earlier on-policy distillation of memory control relied on dedicated memory tools or compaction protocols that neither the teacher nor the student had seen, which weakened guidance on the teacher side and left the student sampling too few meaningful memory actions. CAMP-II instead routes memory through ordinary Bash file operations that both models already have a prior for, training one policy jointly across five long-horizon environments to a 64.6% average task success rate against 54.2% for the task-reward reference CAMP-I, with transfer to Terminal-Bench 2.1 and SWE-Bench Pro V2.

Long-horizon agents accumulate interaction histories that outgrow the active context, so they must learn what to write down and when to read it back. Earlier on-policy distillation of memory control relied on dedicated memory tools or compaction protocols that neither the teacher nor the student had seen, which weakened guidance on the teacher side and left the student sampling too few meaningful memory actions. CAMP-II instead routes memory through ordinary Bash file operations that both models already have a prior for, training one policy jointly across five long-horizon environments to a 64.6% average task success rate against 54.2% for the task-reward reference CAMP-I, with transfer to Terminal-Bench 2.1 and SWE-Bench Pro V2.