SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs (2025.acl-long)
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| Challenge: | Existing approaches to continuous-space reasoning focus on hard token decoding and suffer from catastrophic forgetting. |
| Approach: | They propose a method that generates instance-specific soft thought tokens as the initial chain of thoughts and maps them into the LLM’s representation space via a trainable projection module. |
| Outcome: | The proposed method improves LLM reasoning performance through supervised, parameter-efficient fine-tuning. |
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