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This work proposes an intra-graph mechanism that maps selective KV recomputation onto static NPU graphs, reconciling algorithmic dynamicity with NPU staticity, and develops an inter-graph scheduler to optimize chunk merging and minimize padding with dynamic programming.
Embodied reinforcement learning (RL) improves model capabilities with a pipeline of environment simulation, action generation, and model updates. These stages show heterogeneous CPU and GPU demands, making efficient resource utilization difficult. Recent systems overlap rollout (simulation and generation) with training...
C2KV is proposed, a unified framework for non-prefix KV reuse that jointly optimizes KV cache compression and concatenation that significantly reduces KV cache storage and transfer costs.
Evidence-First Reflection (EFR), a two-stage reflector that explicitly decouples action-induced visual differences extraction from outcome verification, makes reflection better grounded in screen transitions, while reducing both visual search complexity and reasoning burden.
Gleam is a novel and network-efficient framework for task-generic GPU sharing across local-area CUDA devices, with three key contributions that reduce bandwidth overhead in CUDA API remoting through automatic model weight caching, and mitigate accumulated latency from frequent API calls by asynchronous execution.
This work proposes a novel topology named the Balanced Sparse Tree (BST), which is a topology characterized by symmetric design and sparse connections, motivated by hypergraph theory and Steiner Systems, and demonstrates the superiority of BST over the state-of-the-art in network scale, latency, bandwidth, and cost.
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