# Calibrated Clipping

This work studies the training stability of full-pipeline FP8 reinforcement learning for LLMs, identifies that compounded FP8 quantization noise distorts the importance ratio so that negative-advantage tokens are over-clipped at the lower bound and their gradients are erroneously zeroed, and proposes Calibrated Clipping, which dynamically realigns clipping bounds by matching the lower-bound clipping quantile of a BF16 reference and rebalancing the upper bound, eliminating entropy surges and restoring near-BF16 performance across GRPO and DAPO, 8B to 32B models, and multiple FP8 scaling granularities, while reaching up to 1.5x the BF16 training throughput.

This work studies the training stability of full-pipeline FP8 reinforcement learning for LLMs, identifies that compounded FP8 quantization noise distorts the importance ratio so that negative-advantage tokens are over-clipped at the lower bound and their gradients are erroneously zeroed, and proposes Calibrated Clipping, which dynamically realigns clipping bounds by matching the lower-bound clipping quantile of a BF16 reference and rebalancing the upper bound, eliminating entropy surges and restoring near-BF16 performance across GRPO and DAPO, 8B to 32B models, and multiple FP8 scaling granularities, while reaching up to 1.5x the BF16 training throughput.