# miniTE

Native NVFP4 reinforcement learning starts with normal reward gains, then develops abnormal entropy, a widening train–sampling log-probability gap and falling reward late in training. miniTE separates quantization policy, linear layers and compute backends, and adds per-token scaling, contiguous layouts and amax reuse; on 8×B300, both miniTE paths with BF16 backward reach roughly 1.33–1.37× paired speedup over BF16 on complete learner updates, while a separate fixed-output serving test reaches about 1.64× throughput. These learning and system results come from different configurations and do not yet establish an end-to-end RL speedup.

Native NVFP4 reinforcement learning starts with normal reward gains, then develops abnormal entropy, a widening train–sampling log-probability gap and falling reward late in training. miniTE separates quantization policy, linear layers and compute backends, and adds per-token scaling, contiguous layouts and amax reuse; on 8×B300, both miniTE paths with BF16 backward reach roughly 1.33–1.37× paired speedup over BF16 on complete learner updates, while a separate fixed-output serving test reaches about 1.64× throughput. These learning and system results come from different configurations and do not yet establish an end-to-end RL speedup.