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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.19140 |
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| _version_ | 1866915357392896000 |
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| author | Wang, Barry Schwarzschild, Avi Robey, Alexander Payani, Ali Fleming, Charles Sun, Mingjie Ippolito, Daphne |
| author_facet | Wang, Barry Schwarzschild, Avi Robey, Alexander Payani, Ali Fleming, Charles Sun, Mingjie Ippolito, Daphne |
| contents | Retrofitting large language models (LLMs) with new behaviors typically requires full finetuning or distillation-costly steps that must be repeated for every architecture. In this work, we introduce Command-V, a backpropagation-free behavior transfer method that copies an existing residual activation adapter from a donor model and pastes its effect into a recipient model. Command-V profiles layer activations on a small prompt set, derives linear converters between corresponding layers, and applies the donor intervention in the recipient's activation space. This process does not require access to the original training data and needs minimal compute. In three case studies-safety-refusal enhancement, jailbreak facilitation, and automatic chain-of-thought reasoning--Command-V matches or exceeds the performance of direct finetuning while using orders of magnitude less compute. Our code and data are accessible at https://github.com/GithuBarry/Command-V/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Command-V: Pasting LLM Behaviors via Activation Profiles Wang, Barry Schwarzschild, Avi Robey, Alexander Payani, Ali Fleming, Charles Sun, Mingjie Ippolito, Daphne Machine Learning Retrofitting large language models (LLMs) with new behaviors typically requires full finetuning or distillation-costly steps that must be repeated for every architecture. In this work, we introduce Command-V, a backpropagation-free behavior transfer method that copies an existing residual activation adapter from a donor model and pastes its effect into a recipient model. Command-V profiles layer activations on a small prompt set, derives linear converters between corresponding layers, and applies the donor intervention in the recipient's activation space. This process does not require access to the original training data and needs minimal compute. In three case studies-safety-refusal enhancement, jailbreak facilitation, and automatic chain-of-thought reasoning--Command-V matches or exceeds the performance of direct finetuning while using orders of magnitude less compute. Our code and data are accessible at https://github.com/GithuBarry/Command-V/. |
| title | Command-V: Pasting LLM Behaviors via Activation Profiles |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.19140 |