When Reasoning Supervision Hurts: TTCW-Based Long-Form Literary Review Generation

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, Qwen3-8B, and Nemotron-8B. Across all three models, no-trace supervision yields higher score imitation fidelity and near-complete ...

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