Papers with Proxy Tuning
Adaptive Weighted Proxy Tuning: Efficient Gray-Box Steering for Image Captioning. (2026.acl-industry)
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| Challenge: | Proxy tuning is a decoding-time approach that fails to account for instance-specific variations in model certainty and domain shift. |
| Approach: | They propose a gray-box steering framework that dynamically modulates the logit contributions of a large base model, a fine-tuned expert, and an untune . |
| Outcome: | Adaptive Weighted Proxy Tuning achieves performance parity with fine-tuned models while remaining parameter-free. |
Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion (2026.acl-long)
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| Challenge: | Proxy Tuning offers a logit-level strategy for introducing scaling effects, but it often fails in LRL settings because the large model’s weak LRL competence might overwhelm the knowledge of specialized smaller models. |
| Approach: | They propose a logit-based framework that balances LRL competence from a continually pretrained small model, task competence from high-resource language instruction tuning, and the scaling benefits of large models. |
| Outcome: | Experiments across four model families and eight LRLs show that TriMix outperforms single-model baselines and Proxy Tuning. |