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Slow-Fast Multi-Teacher On-Policy Distillation (SF-MOPD), which couples a fast model, the current student updated directly by each teacher, with a slow model, an exponential moving average of the student, which effectively mitigates capability interference, enhances specialized multimodal capabilities, and reduces the...
This work proposes a causal framework based on activation steering to actively probe and manipulate internal visual representations, revealing a divergence in concept encoding: entity knowledge is distinctively localized, whereas abstract concepts are globally distributed across the network.
Experimental results demonstrate that S2V can consistently improve LVU performance across multiple LVU benchmarks, outperforming both general MLLMs and reasoning-based methods not only in LVU accuracy but also in training and inference efficiency.
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