SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling
📰 ArXiv cs.AI
SortedRL accelerates RL training for LLMs by optimizing the rollout phase with online length-aware scheduling
Action Steps
- Identify the bottleneck in RL training, typically the rollout phase
- Implement online length-aware scheduling to prioritize shorter trajectories
- Optimize autoregressive generation and reduce synchronization overhead
- Evaluate the impact of SortedRL on training time and model performance
Who Needs to Know This
Machine learning researchers and engineers working on LLMs can benefit from this technique to improve training efficiency, while software engineers can apply the scheduling approach to similar problems
Key Insight
💡 Optimizing the rollout phase with online length-aware scheduling can significantly improve RL training efficiency for LLMs
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🚀 SortedRL accelerates RL training for LLMs by 70%
Key Takeaways
SortedRL accelerates RL training for LLMs by optimizing the rollout phase with online length-aware scheduling
Full Article
Title: SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling
Abstract:
arXiv:2603.23414v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) has shown strong promise for enhancing the reasoning abilities of large language models (LLMs), particularly in tasks requiring long chain-of-thought generation. However, RL training efficiency is often bottlenecked by the rollout phase, which can account for up to 70% of total training time when generating long trajectories (e.g., 16k tokens), due to slow autoregressive generation and synchronization overhead
Abstract:
arXiv:2603.23414v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) has shown strong promise for enhancing the reasoning abilities of large language models (LLMs), particularly in tasks requiring long chain-of-thought generation. However, RL training efficiency is often bottlenecked by the rollout phase, which can account for up to 70% of total training time when generating long trajectories (e.g., 16k tokens), due to slow autoregressive generation and synchronization overhead
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