A new research effort targets full-pipeline FP8 reinforcement learning for large language models, addressing the difficulty of maintaining stability across an FP8 RL pipeline even though FP8 quantization can accelerate RL training. The work notes that RL has become a key technique for improving LLM reasoning and agentic abilities, and that prior efforts focused on only part of the pipeline.
Reinforcement learning (RL) has become a key technique for improving the reasoning and agentic abilities of large language models (LLMs). Although FP8 quantization can accelerate RL training, maintaining stability throughout an FP8 RL pipeline remains challenging. While previous works have focused o
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