Table 10 reports the full zero-shot evaluation row for GPT-Neo 2.7B.

openai/gpt-5.6-luna-pro

01a3645ba6

The evaluation was run on an NVIDIA L4 rather than paper-specific hardware; this numerical reproduction does not establish hardware speed or the paper's hardware conditions. · The paper labels the columns acc but does not specify ordinary versus length-normalized accuracy per task. The plan uses ordinary acc,none based on that label and the evaluator definitions; raw acc_norm alternatives are retained for independent assessment, and several paper values resemble normalized alternatives. · The exact historical checkpoint/evaluation provenance and any undocumented preprocessing differences behind the paper's printed row were not reconstructed beyond the released checkpoint, current lm-eval 0.4.2 task definitions, and recorded zero-shot conditions. · The paper reports only an 'acc' label for multiple-choice tasks and does not specify ordinary versus length-normalized accuracy; this is decisive for HellaSwag, OpenBookQA, and possibly ARC-C. · The run used an NVIDIA L4 and current lm-eval 0.4.2 against a released checkpoint revision; the paper's exact hardware, historical checkpoint provenance, dataset versions, and preprocessing were not reconstructed. · The declared workload lists 29 complete Table 10 model rows across three groups, but raw execution evidence covers only the assigned GPT-Neo 2.7B row; no broader multi-row completion should be inferred. · Parser bindings were corrected after the full run to read the native raw evaluator fields rather than risk double-scaling normalized output. The repair is mechanically supported and did not alter the extracted values, but it is a post-result protocol revision that does not resolve the source metric ambiguity. · The PDF directly shows a complete GPT-Neo 2.7B row, and the captured execution completed all seven assigned zero-shot tasks on the released GPT-Neo 2.7B checkpoint and recomputed the seven-task average. Several quantities are close to the table, including LAMBADA perplexity and accuracy, PIQA, and ARC-E. However, the full numerical row is not established as reproduced: ordinary HellaSwag and OpenBookQA accuracies differ substantially, while their length-normalized alternatives happen to match the printed values; ARC-C also differs materially. Because the paper labels these columns only 'acc' and does not specify ordinary versus normalized accuracy, the quantities are not fully comparable. The average consequently also compares different or unresolved operands. The source-reporting claim is therefore not contradicted, but the complete row cannot be verified as a reproducible numerical result under the recorded protocol.