- Updated: August 25, 2026
- 2 min read
A-3PO: Accelerating Asynchronous LLM Training with Staleness‑aware Proximal Policy Approximation
In this article we present A‑3PO (Approximated Proximal Policy Optimization), a novel method that removes the computational overhead of the proximal policy in decoupled PPO by approximating it through simple interpolation. The approach delivers up to 1.8× speed‑up in asynchronous LLM training while preserving performance. Detailed explanations, experimental results, and code links are provided below.
Background
Decoupled PPO has become a popular reinforcement‑learning algorithm for handling high data staleness in asynchronous settings. However, the additional forward pass required for the proximal policy introduces a significant computational cost for large language models.
Our Contribution: A‑3PO
We observe that the proximal policy serves primarily as a trust‑region anchor. By approximating this anchor via linear interpolation between the behavior and target policies, we eliminate the extra forward pass. This simple modification yields a 1.8× training speed increase without sacrificing model quality.
Implementation
The method is integrated into the open‑source RL training system AReaL. Users can enable A‑3PO with a single configuration change.
Results
Benchmarks on standard LLM workloads demonstrate comparable final performance to full decoupled PPO while achieving the aforementioned speedup.
Conclusion
A‑3PO offers a practical, low‑overhead solution for accelerating asynchronous LLM training, making large‑scale RL more efficient.
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Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.