What is recorded here?
Decoupled continuous generation from training, using interruptible rollouts, bounded data staleness, dynamic batching and a decoupled PPO objective for trajectories generated across policy versions.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits asynchronous RL and decoupled PPO. Weight updates can interrupt generation, discard/recompute KV caches and create mixed-policy trajectories; staleness must be constrained, not ignored. Up to2.57fold acceleration and matched accuracy concern selected math/code experiments, not universal convergence or arbitrary off-policy stability.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Wei Fu; Jiaxuan Gao; Xujie Shen; Chen Zhu; Zhiyu Mei; Chuyi He; Shusheng Xu; Guo Wei; Jun Mei; Jiashu Wang; Tongkai Yang; Binhang Yuan; Yi Wu
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
AReaL: asynchronous rollouts with staleness-aware policy updates
Decoupled continuous generation from training, using interruptible rollouts, bounded data staleness, dynamic batching and a decoupled PPO objective for trajectories generated across policy versions.
Scientific scope
Inherits asynchronous RL and decoupled PPO. Weight updates can interrupt generation, discard/recompute KV caches and create mixed-policy trajectories; staleness must be constrained, not ignored. Up to2.57fold acceleration and matched accuracy concern selected math/code experiments, not universal convergence or arbitrary off-policy stability.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Wei Fu; Jiaxuan Gao; Xujie Shen; Chen Zhu; Zhiyu Mei; Chuyi He; Shusheng Xu; Guo Wei; Jun Mei; Jiashu Wang; Tongkai Yang; Binhang Yuan; Yi Wu
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.