A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908868689264640 |
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| author | Li, Xiaocan Wu, Shiliang Shen, Zheng |
| author_facet | Li, Xiaocan Wu, Shiliang Shen, Zheng |
| contents | Decoupled PPO has been a successful reinforcement learning (RL) algorithm to deal with the high data staleness under the asynchronous RL setting. Decoupled loss used in decoupled PPO improves coupled-loss style of algorithms' (e.g., standard PPO, GRPO) learning stability by introducing a proximal policy to decouple the off-policy correction (importance weight) from the policy update constraint (trust region). However, the proximal policy requires an extra forward pass through the model at each training step, creating a computational overhead for large language models training. We observe that since the proximal policy only serves as a trust region anchor between the behavior and target policies, we can approximate it through simple interpolation without explicit computation. We call this approach A-3PO (APproximated Proximal Policy Optimization). A-3PO eliminates this overhead, accelerating training by 1.8x speedup while maintaining comparable performance. Code \& off-the-shelf example are contributed to the open-source RL training system AReaL at: https://github.com/inclusionAI/AReaL/blob/v1.0.0.rc1/docs/algorithms/prox_approx.md |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation Li, Xiaocan Wu, Shiliang Shen, Zheng Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Decoupled PPO has been a successful reinforcement learning (RL) algorithm to deal with the high data staleness under the asynchronous RL setting. Decoupled loss used in decoupled PPO improves coupled-loss style of algorithms' (e.g., standard PPO, GRPO) learning stability by introducing a proximal policy to decouple the off-policy correction (importance weight) from the policy update constraint (trust region). However, the proximal policy requires an extra forward pass through the model at each training step, creating a computational overhead for large language models training. We observe that since the proximal policy only serves as a trust region anchor between the behavior and target policies, we can approximate it through simple interpolation without explicit computation. We call this approach A-3PO (APproximated Proximal Policy Optimization). A-3PO eliminates this overhead, accelerating training by 1.8x speedup while maintaining comparable performance. Code \& off-the-shelf example are contributed to the open-source RL training system AReaL at: https://github.com/inclusionAI/AReaL/blob/v1.0.0.rc1/docs/algorithms/prox_approx.md |
| title | A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2512.06547 |