Accelerating LLM Reasoning: Slicing RL Training Gaps from 74% to 3%

Scaling reinforcement learning is critical for long chain-of-thought (CoH) reasoning in LLMs, but hardware efficiency has remained a massive hurdle. In traditional on-policy RL, the rollout phase—dominated by slow autoregressive generation—can consume 70% of total training time, creating massive "computational bubbles" as systems wait for the longest samples to finish.

SortedRL addresses this through an online length-aware scheduling strategy. By dynamically batching samples with similar generation lengths and implementing oversubscription mechanisms, it minimizes hardware idle time while maintaining near-perfect policy alignment.

Key Results:

Bubble Ratio: Reduced from 74.0% to as low as 3.37%.
Throughput: Boosted rollout speed by up to 39.48%.
Performance: LLaMA-3.1-8B-Instruct achieved high baseline scores using 40.74% fewer samples.
Accuracy: 18.4% improvement on competition-level math benchmarks (AIME 2024).

This research proves that scheduling optimizations are as vital as algorithm architecture for scalable AI reasoning.

https://arxiv.org/pdf/2603.23414 All my links: https://linktr.ee/learnbydoingwithsteven

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