VERDICT: NEARLY_IDENTICAL lined-up score: 93/100 (sub-agent) [eval_step >= train_step: eval@281 >= train@266]
[STRATEGY-GEN cross-domain: train=writing-style ranking -> eval=math proof-strategy ranking]
REPLACES the earlier 13-vs-6-turn debug-heavy pair (was 60/100). Now length-matched, both 5 turns,
both trained checkpoints (eval r1.0, train r0.57 nDCG).
Same 5 turns: inspect context (len + preview docs) -> parse each ‘— doc_id: --- ' into
(doc_id, text) -> define 0-N relevance prompt + batch llm_query_batched (batch_size=20) with try/except
score-parse -> zip+sort desc -> answer[content]=ranked_doc_ids. Same variable names (batch_size, scores,
doc_scores, ranked_doc_ids), same top-10 verification print, same finalize block.
Differences: parse idiom (line-scan vs split('---')), score scale (0-10 vs 0-100), inline query vs query_text.
The stdout content differs by domain (math proofs vs essays) but the code/turn-flow reads nearly identical.