arXiv Artificial Intelligence

Towards Full Pipeline FP8 Reinforcement Learning for LLMs

2 hours ago 23.09.2026 07:00 👁 1 views
Towards Full Pipeline FP8 Reinforcement Learning for LLMs

Quick summary

arXiv:2609.22870v1 Announce Type: cross Abstract: 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 on resolving train-inference mismatches using correction techniques like TIS, we reveal that full-pipeline FP8 RL still suffers from severe training instability, manifesting as anomalous mid-training entropy surges and garbled outputs. We trace this i

Key takeaways

  • arXiv:2609.22870v1 Announce Type: cross Abstract: 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 on resolving train-inference mismatches using correction techniques like TIS, we reveal that full-pipeline FP8 RL still suffers from severe training instability, manifesting as anomalous mid-training entropy surges and garbled outputs.

Why it matters

“Towards Full Pipeline FP8 Reinforcement Learning for LLMs” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗