Papers by Leyan Pan
Superficial Self-Improved Reasoners Benefit from Model Merging (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) rely heavily on large-scale reasoning data, but as data becomes scarce, model self-improvement offers a promising alternative. |
| Approach: | They propose to merge the weights of original and self-improved LLMs to mitigate model collapse and improve generalized reasoning capability. |
| Outcome: | The proposed model merge mitigates model collapse and improves generalized reasoning capability. |