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LLM training bottlenecks? ZipCCL achieves up to 1.18x end-to-end speedups by losslessly compressing communication collectives, without sacrificing model quality.
LLMs can leapfrog state-of-the-art scientific algorithms and human-designed solutions, but only if you scale the evaluation loop, not just the model.
LLM-powered recommendation agents, despite their reasoning prowess, are easily manipulated by contextual biases in high-stakes scenarios like paper review and job recruitment.
Lossless compression can actually *speed up* LLM inference on GPUs, not just shrink model size, thanks to ZipServ's hardware-aware design.
LLM alignment is fundamentally challenged by the dynamic and inconsistent nature of their internal "priority graphs," which adversaries can exploit through context manipulation.
Achieve up to 102% Sharpe Ratio improvement and 17.5% directional accuracy gain by unifying event-centric data construction and decision-oriented fine-tuning with a hierarchical gated reward model.
Naive application of LLM inference optimizations can *hurt* the performance of smaller reasoning models, highlighting the need for RLLM-specific serving strategies.
