Generating diverse solutions is key to human-like reasoning, yet autoregres-sive language models focus on single accurate responses, limiting creativity. Our case study shows their limited zero-shot transferability by fine-tuning small and medium-sized large language models on the Game of 24 and testing them on the Game of 42 datasets. Results revealed that GFlowNets struggle to maintain solution diversity and accuracy, highlighting key limitations in their cross-task generalization and the need for future research in improved transfer learning capabilities. Recent advances have introduced approaches showing significant improvement in LLM reasoning capabilities (Touvron et al., 2023a), including supervised fine-tuning with synthetic datasets (Y u et al.; Y ue et al.), modified decoding mechanisms (Holtzman et al.; Nguyen et al., 2024), and enhanced pretraining data quality (Akter et al., 2024; Trinh et al., 2024). While these approaches demonstrate improved accuracy, they rarely account for the diversity of correct solutions, an essential aspect of human-like reasoning and creativity (Y u et al., 2024a; Hu et al.).

Urban planning faces a critical challenge in balancing city-wide infrastructure needs with localized demographic preferences, particularly in rapidly developing regions. Although existing approaches typically focus on top-down optimization or bottom-up community planning, only some frameworks successfully integrate both perspectives. Our methodology employs a two-tier approach: First, a deterministic solver optimizes basic infrastructure requirements in the city region. Second, four specialized planning agents, each representing distinct sub-regions, propose demographic-specific modifications to a master planner. The master planner then evaluates and integrates these suggestions to ensure cohesive urban development. We validate our framework using a newly created dataset comprising detailed region and sub-region maps from three developing cities in India, focusing on areas undergoing rapid urbanization. The results demonstrate that this hybrid approach enables more nuanced urban development while maintaining overall city functionality.