Author

Tanzila Rahman

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Jul 2026

HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models

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.

H. Mak, Shadan Golestan, H. Le et al. · arXiv.org · 0 citations

Preprint Aug 2026

Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs

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 computation.

Tanzila Rahman, Mehran Taghian Jazi, Yunke Peng et al. · 0 citations