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It is argued that numerical format design is itself a first-class lever for stable FP4 training, and the first systematic study of FP4 LLM pretraining on energy-efficient Huawei Ascend NPUs is presented.
This work introduces TLXML (Task-Level eXplanation of Meta-Learning), a novel framework that extends influence functions to meta-learning settings, enabling task-level explanations of adaptation and inference, and proposes a Gauss-Newton-based approximation.
HiF4 is established as the enabling format for end-to-end FP4 RL post-training, and Rollout Residual Quantization (Rollout-ResQ) is established as the activation-side mechanism that makes the gap to BF16 closable.
This work proposes Residual Fallback Quantization (RFQ), a lightweight activation reconstruction framework that supplements the primary ulta-low-bit activation representation with an auxiliary quantized residual pathway that improves activation fidelity while preserving the efficiency advantages of ultra-low-bit comput...
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