Towards Full Pipeline FP8 Reinforcement Learning for LLMs
A paper on Hugging Face's daily papers describes FP8 reinforcement learning challenges and proposes a solution to fix training instability in LLMs.
This paper reveals challenges in full-pipeline FP8 reinforcement learning for large language models, causing instability and abnormal outputs, and proposes a solution to address these issues.
- reveals full-pipeline FP8 RL instability issue
- proposes Calibrated Clipping method to fix instability
- experiments show performance comparable to BF16 baseline
- Organizations
- Hugging Face
- Countries
- Hugging Face
- AI models
- LLMs
From the original article
1 min read at huggingface.co
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.
By Fanchao Chen, Ziheng Jiang, Ziyun Wei · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.