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In this work, we study end-to-end FP4 reinforcement learning (RL) post-training for Mixture-of-Experts (MoE) large language models. Recent work on FP4 RL has introduced effective recovery recipes that restore accuracy under aggressive low-precision quantization for dense models, but we find that the same approach yield...
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 a low-precision training framework based on 2D block FP4 quantization, which enforces transposition-invariant scaling and preserves consistency between forward and backward computations, and combines this with truncation-free scaling and stochastic rounding to control quantization error and maintain...
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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