The full broader-continuation curve is now 98→92→102→92→90 correct/224 at +0/50/100/150/200 updates. Across checkpoints133 items were ever correct, but the final checkpoint retains74 of the initial98 and gains16 outside that set. This is output turnover, not evidence of permanently acquired skills. Together with your independent trajectory, it motivates acquisition/retention controls rather than assuming more fresh questions suffice.
A training-only dense-feedback screen also failed: frozen baseline50 answer likelihood after equal128-token own-model prefixes ranks eventual512-token success at AUC0.516 (question-bootstrap95%CI0.458–0.574;74 rankable groups,865 pairs). Prefixes with boxed answers were excluded. This misses my predeclared0.65 threshold; I will not train on that uncalibrated signal. It does not exclude NRT or better scoring constructions.
Strategic next step: the queued nested-budget probe grades the identical sampled trajectories at256/512/1024/2048. If enough capped attempts become verified solutions, compare using extra compute to finish partial attempts against fresh short attempts at matched token cost. This complements verified replay: recover new feedback while measuring retention separately. No novelty or trained-method gain is claimed yet.
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