GAC: Stabilizing Asynchronous RL Training for LLMs via Gradient Alignment Control
📰 ArXiv cs.AI
Learn to stabilize asynchronous RL training for LLMs using Gradient Alignment Control (GAC) to improve training dynamics
Action Steps
- Apply Gradient Alignment Control (GAC) to asynchronous RL training to reduce variance in policy updates
- Implement asynchronous execution for scaling RL to large model workloads
- Analyze training dynamics to identify potential issues with naive asynchrony
- Configure GAC hyperparameters to optimize training stability
- Test GAC on various LLMs and RL tasks to evaluate its effectiveness
Who Needs to Know This
Researchers and engineers working on large language models (LLMs) and asynchronous reinforcement learning (RL) can benefit from this technique to improve training stability and efficiency
Key Insight
💡 GAC can mitigate the negative effects of asynchrony on RL training dynamics
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🚀 Stabilize asynchronous RL training for LLMs with Gradient Alignment Control (GAC) 🤖
Key Takeaways
Learn to stabilize asynchronous RL training for LLMs using Gradient Alignment Control (GAC) to improve training dynamics
Full Article
Title: GAC: Stabilizing Asynchronous RL Training for LLMs via Gradient Alignment Control
Abstract:
arXiv:2603.01501v2 Announce Type: replace-cross Abstract: Asynchronous execution is essential for scaling reinforcement learning (RL) to modern large model workloads, including large language models and AI agents, but it can fundamentally alter RL optimization behavior. While prior work on asynchronous RL focuses on training throughput and distributional correction, we show that naively applying asynchrony to policy-gradient updates can induce qualitatively different training dynamics and lead t
Abstract:
arXiv:2603.01501v2 Announce Type: replace-cross Abstract: Asynchronous execution is essential for scaling reinforcement learning (RL) to modern large model workloads, including large language models and AI agents, but it can fundamentally alter RL optimization behavior. While prior work on asynchronous RL focuses on training throughput and distributional correction, we show that naively applying asynchrony to policy-gradient updates can induce qualitatively different training dynamics and lead t
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