SPEED-RL: Faster Training of Reasoning Models via Online Curriculum Learning

• URL: http://arxiv.org/abs/2506.09016v2
• Date: Tue, 08 Jul 2025 00:02:06 GMT
• Title: SPEED-RL: Faster Training of Reasoning Models via Online Curriculum Learning
• Authors: Ruiqi Zhang, Daman Arora, Song Mei, Andrea Zanette,
• Abstract summary: Training large language models with reinforcement learning (RL) against verifiable rewards significantly enhances their reasoning abilities.<n>We introduce Selective Prompting with Efficient Estimation of Difficulty (SPEED), an adaptive online RL curriculum that selectively chooses training examples of intermediate difficulty to maximize learning efficiency.<n> Empirically, our efficient implementation leads to 2x to 6x faster training without degrading accuracy, requires no manual tuning, and integrates seamlessly into standard RL algorithms.
• Score: 30.90778400005588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training large language models with reinforcement learning (RL) against verifiable rewards significantly enhances their reasoning abilities, yet remains computationally expensive due to inefficient uniform prompt sampling. We introduce Selective Prompting with Efficient Estimation of Difficulty (SPEED), an adaptive online RL curriculum that selectively chooses training examples of intermediate difficulty to maximize learning efficiency. Theoretically, we establish that intermediate-difficulty prompts improve the gradient estimator's signal-to-noise ratio, accelerating convergence. Empirically, our efficient implementation leads to 2x to 6x faster training without degrading accuracy, requires no manual tuning, and integrates seamlessly into standard RL algorithms.

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