VERDICT: MOSTLY_SAME lined-up score: 84/100 (sub-agent) [eval_step >= train_step: eval@91 >= train@54] eval@91/16 (t6, r0.91) <-> train@54/36 (t6, r0.81), both trained checkpoints, matched 6 turns. Same 6-step plan turn-for-turn: inspect context -> parse (user_id, instance) -> {rlm,llm}_query_batched classify into the 6 categories -> build user->labels map + filter target-label users -> nested-loop emit (u1<u2) pairs -> answer. Real (not just size) diffs: rlm_query_batched vs llm_query_batched, str vs int user ids, set vs list label store, different classify-prompt wording. Hence MOSTLY_SAME not NEARLY_IDENTICAL.