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Shanghai Jiao Tong University
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Saliency-guided sparse updates, focusing on high-magnitude activations in query and key vectors, unlock significant performance gains in long-context RL, outperforming uniform update strategies.
Closed-loop evaluation reveals how VLMs for autonomous driving handle the messy reality of off-road deviations and out-of-distribution states, something static QA datasets can't capture.
LLMs can now synthesize high-performance kernels for niche hardware like NPUs, even with limited data, thanks to a self-evolving agent that bootstraps and refines code via value-driven reinforcement learning.
FineRMoE achieves 6x higher parameter efficiency, 281x lower prefill latency, and 136x higher decoding throughput compared to strong baselines, demonstrating a significant leap in MoE performance.
