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SCALE (Selective Control of Adaptation via Local Entropy), an entropy-guided adaptation-strength-control method that freezes the pretrained model and the SFT delta and learns bounded token- and module-specific gates by minimizing predictive entropy alone is proposed.
This paper proposes MSCE, a training-free Memory--Skill Co-Evolution framework that organizes agent experience into grounded step traces, reusable procedural policies, and declarative environmental cognition, and introduces reflection-weighted value backfilling.
This paper makes the first attempt to fine-tune MoE models with MoE-style low-rank adaptation via a dual-channel Routing-Conditioned Projection module, which reuses base router activations to inform LoRA routing and introduces a single global LoRA expert pool shared across all layers.
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