LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment

📰 ArXiv cs.AI

Learn to improve LLM reinforcement learning with LearnAlign, a novel method for data selection with improved gradient alignment, to enhance reasoning abilities

advanced Published 28 Apr 2026

Action Steps

Implement LearnAlign to select representative training data for RLVR post-training
Use gradient alignment to identify learnable data
Apply LearnAlign to existing LLM reinforcement learning pipelines to improve data efficiency
Evaluate the performance of LearnAlign using metrics such as gradient alignment and reasoning accuracy
Compare the results of LearnAlign with other data selection methods to determine its effectiveness

Who Needs to Know This

Machine learning engineers and researchers working on LLMs can benefit from LearnAlign to improve the efficiency of their reinforcement learning pipelines

Key Insight

💡 LearnAlign improves the efficiency of LLM reinforcement learning by selecting representative and learnable training data using gradient alignment

Full Article

Title: LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment

Abstract:
arXiv:2506.11480v4 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing LLMs' reasoning abilities, yet its data inefficiency remains a major bottleneck. To address this critical yet challenging issue, we present a novel gradient-alignment-based method, named LearnAlign, which intelligently selects the learnable and representative training reasoning data for RLVR post-training. To overcome the well-known response-len

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