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The results suggest that dense to MoE adaptation with dynamic expert deactivation is a practical direction for reducing active VLA model size without severe performance loss.
STEP-OPD is proposed, an on-policy distillation framework for image generation that extends the student's learning target beyond the teacher and introduces explicit constraints on its internal representation evolution, showing that output extrapolation enables beyond-teacher learning.
This work develops HarnessBandit, an online scheduler that selects one harness per optimizer step, and improves over mixed-batch multi-harness training on both benchmarks, while training diagnostics indicate that learnability and transferability provide distinct, evolving signals.
BQ-LoRA is proposed, a low-rank adaptation framework that organizes trajectory updates through a local behavior quotient manifold and contains two modules, i.e., behavior quotient balancing (BQB) and decision preserving compression (DPC).
TAC-Merge is proposed for tracing and coordinating cross-layer influence in multimodal model merging and demonstrated the effectiveness in consolidating complementary expert capabilities and supporting generalization to unseen tasks.
This work argues that closing this gap requires alignment at every level of the pipeline, including execution, supervision, optimization, and evaluation, rather than scale alone, and presents Wuying-Browser-Agent, a unified framework that addresses each of these levels.
Method, an egocentric world-action simulator that synthesizes controllable, high-quality manipulation videos to expand scarce real-world training data, is presented, demonstrating that the synthesized data substantially improve downstream WAM generalization.
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