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GAC: Stabilizing Asynchronous RL Training for LLMs via Gradient Alignment Control
🤖AI Summary
Researchers propose GAC (Gradient Alignment Control), a new method to stabilize asynchronous reinforcement learning training for large language models. The technique addresses training instability issues that arise when scaling RL to modern AI workloads by regulating gradient alignment and preventing overshooting.
Key Takeaways
- →Asynchronous RL training can cause severe instability in large language model optimization despite being essential for scaling.
- →The instability manifests as persistently high cosine similarity between consecutive policy gradients, unlike stable synchronized training.
- →GAC uses gradient projection to regulate training progress along stale-aligned directions and prevent divergence.
- →The method provides convergence guarantees under bounded staleness conditions.
- →Empirical results show GAC recovers stable training dynamics matching synchronized baselines even at high staleness levels.
#reinforcement-learning#large-language-models#training-optimization#gradient-alignment#asynchronous-training#ai-scaling#llm-training#research
Read Original →via arXiv – CS AI
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