Do GFlowNets Transfer? Case Study on the Game of 24/42
This addresses the problem of limited creativity in AI reasoning for researchers, but it is incremental as it highlights limitations in an existing method.
The study investigated the zero-shot transferability of GFlowNets for generating diverse solutions, using fine-tuned language models on the Game of 24 and testing on the Game of 42, finding that they struggle to maintain diversity and accuracy.
Generating diverse solutions is key to human-like reasoning, yet autoregressive language models focus on single accurate responses, limiting creativity. GFlowNets optimize solution generation as a flow network, promising greater diversity. 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.