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Simon Fraser University, Canada
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Rollout activation quantization, not training-side errors, is the hidden culprit behind FP4 RL accuracy degradation, and a clever residual correction can bridge the gap to full precision.
LLMs can be effectively combined with graph-based methods to capture both semantic and structural information in tables, leading to state-of-the-art performance in table annotation tasks.