Automatic evaluation of long-form literary writing remains challenging, particularly for creativity-related dimensions such as originality and flexibility.
We address the lack of large-scale supervision for structured Torrance Test of Creative Writing (TTCW)-based evaluation by constructing a dataset of 263{,}911 long-form stories, each paired with scalar scores and meta-synthesised review comments across 14 TTCW-based dimensions.
Using this dataset, we study whether reviewer-style reasoning traces improve structured literary review generation across Qwen3-4B, Qwen-8B, and Nemotron-8B.
Across all three models, no-trace supervision yields higher score imitation fidelity and near-complete report generation, whereas trace supervision reduces completion to $85.9$--$90.9\%$.
Failure analysis shows that $88.6\%$ of failed reasoning generations contain semantic repetition loops inside the trace.
These loops concentrate in particular story--criterion interactions and progressively harden after onset, while comparable repetition is rare in the supervision data.
Our results suggest that, for long-form tasks requiring strictly structured outputs, explicit reasoning traces can introduce generation instability that outweighs their overall benefit.