A team is developing a reward model for a conversational AI that acts as a creative writing partner. The AI's responses can vary greatly in complexity, from short, simple suggestions (e.g., 'How about a character named Alex?') to long, intricate paragraphs describing a scene with complex sentence structures and rich vocabulary. The team is currently using a fixed-length segmentation strategy (e.g., every 50 words) to collect human feedback. They are finding that the quality of the human feedback is inconsistent; for simple segments, the feedback is sparse, while for complex segments, the feedback is often incomplete because the segment contains too many distinct ideas to evaluate with a single score.

Based on this scenario, critique the team's current fixed-length segmentation approach. Then, argue for or against the adoption of a dynamic segmentation strategy where segment boundaries are determined by content complexity. Justify your position by explaining how your recommended approach would lead to a more (or less) effective reward model in this specific context.