VERDICT: NEARLY_IDENTICAL lined-up score: 92/100 (sub-agent) [REPLACES the 11-vs-4-turn mismatch pair]
Length-matched (both 5 turns), both TRAINED checkpoints, both reward 1.0. [STRATEGY-GEN cross-domain:
train=trec classify @32k -> eval=spam classify @132k]
Same 5 turns: peek context -> split lines + filter by ‘User: ' -> parse after 'Instance: ' + build
per-item classify prompts + batched llm_query_batched -> .count() the two target labels -> answer[content]=
'Answer: X is more common than Y'. Sub-agent: 'the eval rollout is essentially the train rollout with the labels swapped.'
Differences: user id (32486 vs 62297), label vocab (spam/ham vs 6 TREC categories), batch_size (20 vs 5),
one rephrased batching loop. token_lcs 0.514 (lower than the old mismatch pair's 0.639 because token_lcs-F1
rewarded the longer eval; but this pair actually READS far more identical -- 92 vs 55 lined-up).