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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.
