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PEARL is an asynchronous agentic RL system that coordinates external resource elasticity, temporary reuse of idle training GPUs, and adaptive PD execution, and maintains a unified GPU--worker--role state and uses runtime profiles to predict rollout batch completion time.
This work identifies the high-sparsity trap, a unified acceleration framework for visual generation that combines a short sparse warm-up, few-step trajectory-mixed distillation, and FP8 quantization with fused kernels, and sustains attention sparsity with strong visual quality on long-sequence 720P generation across Wa...
SCOPE is a training-free sparse attention framework that combines 3D-RoPE-aligned key subspace clustering with online per-head Top-$k$ estimation for efficient video-DiT inference and consistently outperforms existing training-free baselines in both fidelity and latency.
Diffusion Transformers (DiTs) achieve strong video generation quality, but their dense spatiotemporal self-attention scales quadratically with sequence length and quickly becomes the dominant inference bottleneck. Sparse low-rank hybrids alleviate this cost by combining a local sparse branch with a global compressed br...
Shiva-DiT, based on Residual-Based Differentiable Top-k Selection, provides four fidelity-latency operating points and reaches a 1.54x wall-clock speedup with competitive fidelity.
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.
A causal interpretability framework is introduced that reveals that the tokens encoding semantics at the input need not be those that maintain them during generation, but also provides a causal view of internal mechanisms in diffusion transformers.
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. In practice, feature engineering remains...
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