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Achieving real-time ASR performance on edge devices, VibeVoice-ASR-BitNet outpaces Whisper.cpp by up to 2.3x while only modestly sacrificing accuracy.
Experiential Learning outperforms traditional reinforcement learning by providing richer feedback, leading to better generalization and reduced reward hacking in LLM training.
ReOPD transforms costly multi-turn interactions into a reusable offline resource, achieving up to 4× faster rollouts while preserving accuracy.
Language models can learn directly from real-world user interactions, boosting performance without human annotations or simulated environments.
By rethinking RLHF, MicroCoder-GRPO enables smaller code generation models to rival larger counterparts, achieving significant performance gains and revealing 34 training insights.
1.58-bit LLMs are surprisingly more resilient to sparsity than their full-precision counterparts, opening new avenues for extreme compression.
Unlock 33% faster LLM inference on commodity GPUs with SlideSparse, which finally brings hardware-accelerated (2N-2):2N sparsity to the masses, bridging the accuracy gap left by NVIDIA's strict 2:4 pruning.
