vLLM says same weights do not guarantee the same policy, because inference optimizations can change the logprobs that training recomputes. The project says it makes rollout and training bitwise identical while keeping high-performance inference paths. The post is a thread that links to a vLLM blog article.
1/ 🚀 New on the vLLM blog: Finish RL Training Faster — Bitwise-Consistent, High-Performance, Agent-Driven
Same weights don't guarantee the same policy: inference optimizations can change the logprobs that training recomputes.
We make rollout and training bitwise identical while retaining high-performance inference paths. Here's how 🧵
Source: vLLM · x.comPublished · added here
