Papers by Guangnian Wan

1 papers
CoT-Valve: Length-Compressible Chain-of-Thought Tuning (2025.acl-long)

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Challenge: Wei et al., 2022) have developed a powerful method for enhancing the reasoning capabilities of large language models.
Approach: They propose to use a tuning and inference strategy to control the length of reasoning chains by a parameter space direction to control their length.
Outcome: The proposed method reduces reasoning chains on GSM8K from 741 to 225 tokens with a minor performance drop (95.07% to 94.92%) and on AIME from 6827 to 4629 tokens, with only one additional incorrect answer.

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