Researchers have investigated why large language models struggle with planning puzzles like the Tower of Hanoi, particularly a variant where initial and goal states are complex. By training smaller Transformers on precomputed solutions, they found that these models develop an emergent "world model" of the puzzle's state space, which is crucial for solving it. However, when applying this to larger models like Qwen3.6-27B and DeepSeek-R1-Distill-Qwen-32B, the study revealed that while these models encode a similar world model, their performance degrades significantly due to a decaying representation of this model during the planning process. The research suggests that improving the maintenance of these internal representations could enhance the models' reasoning capabilities. AI
IMPACT Highlights a key limitation in current LLMs' reasoning and planning abilities, suggesting a path for future improvements in model architecture and representation maintenance.
RANK_REASON Research paper analyzing LLM limitations on a specific reasoning task. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- DeepSeek-R1-Distill-Qwen-32B
- Hugging Face
- Qwen3.6-27B
- Sierpiński triangle
- Tower of Hanoi
- transformers
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