Papers by Congfeng Cao
Fusion Training for Mathematical Generalization in Large Language Models (2026.acl-srw)
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| Challenge: | Existing efforts to improve reasoning efficiency have focused on switching between simple queries and complex problems. |
| Approach: | They analyze the effects of the training schedule and data ratio between thinking and non-thinking modes and construct a benchmark to test their theory. |
| Outcome: | The proposed model unifies a thinking mode and a non-thinking mode within a single model. |
NeuroAda: Activating Each Neuron’s Potential for Parameter-Efficient Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing methods for parameter-efficient fine-tuning are limited and require computational and memory resources. |
| Approach: | They propose a parameter-efficient fine-tuning method that enables fine-grained model finetunation while maintaining high memory efficiency. |
| Outcome: | The proposed method reduces CUDA memory usage by up to 60% while maintaining high performance. |