Remark. Source horizon and selection hypothesis [ftip-00PB]
Remark. Source horizon and selection hypothesis [ftip-00PB]
The direct observation in [dang2026selecting, Abstract; Sections 3-5 and Tables 1-4] compares 4K, 8K, 12K, and 16K supervision horizons for terminal-agent SFT. In its reported Terminal-Bench v1.0 setting, 12K reaches more tasks than 16K with about thirty percent less training time. The shorter setting under-persisted, the longest setting over-persisted, and the intermediate setting produced the best reported balance. This is a finite terminal-agent observation under one model, teacher corpus, scaffold, and benchmark family; it does not establish a universal horizon, long-term memory, or persistent identity.
The paper's bias-complexity account gives the FTIP hypothesis [dang2026selecting, Sections 5.2 and 6-7; Tables 4-5, 10]. Truncation can increase temporal-supervision bias by removing later decisions, while a longer continuation increases the policy complexity and finite-sample burden. The useful horizon is therefore conditional on the task law, teacher policy, model capacity, verifier, and retained state.
Selective refinement makes the data intervention explicit [dang2026selecting, Sections 6-7; Tables 4-5, 10]. A warm-start model is trained on a shorter horizon, continuation segments are scored by teacher-forced likelihood, and a fixed fraction is retained for longer-horizon refinement. The score measures compatibility with the warm-start policy, not intrinsic truth. High-likelihood selection can improve repeatability; the FTIP protocol tests whether low-likelihood or random selection preserves breadth, rare strategies, and recovery behavior.
A run is premature when it terminates before the declared evidence or recovery condition, productive when it continues through bounded evidence gathering, verification, and recovery, and over-persistent when additional work repeats or explores after task-relevant value has saturated. Record coverage loss separately: a selector can improve repeatability while deleting hard tasks or unusual recovery paths. These terms describe observable behavior; they are not claims about an agent's memory or inner objective.