general family · filed under post-training
Asynchronous reinforcement learning
An asynchronous RL training approach that overlaps rollout generation and training; some implementations explicitly decouple training from inference across devices.
Also called asynchronous RL architecture, asynchronous training paradigm, fully asynchronous training, Asynchronous training.
- sources
- 6
- models
- 5
- labs adopt it
- 3
- strongest
- core
How sources treat it
One count per evidence span, weakest treatment to strongest.
Documented in
Further reading
Picked by hand, not extracted: where to read more, not evidence for anything on this page.
Evidence
7 spans quoted from the sources, strongest treatment first.
we scale batch size and training throughput through fully asynchronous training, processing thousands of long-horizon rollouts and billions of tokens per step
These advances are supported by asynchronous training, mixed-task rollout infrastructure, training–inference consistency mechanisms
It utilized an asynchronous RL architecture that fully decouples training from inference across separate GPU devices
It utilized an asynchronous RL architecture that decouples training from inference and leverages MTP to accelerate rollout generation.
asynchronous RL architecture that fully decouples training from inference across separate GPU devices
we adopt a fully asynchronous training paradigm for Agentic RL
we employ an asynchronous RL architecture that decouples training from inference and leverage multi-token prediction to accelerate rollout generation
Filed alongside
Other methods under post-training :: rollout & RL infrastructure.