DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation

📰 ArXiv cs.AI

Learn how DARE improves reinforcement learning with co-evolved difficulty estimation to prioritize moderately difficult prompts and increase sample efficiency

advanced Published 12 May 2026

Action Steps

Implement DARE to co-evolve difficulty estimation with policy learning
Use difficulty-aware data selection to prioritize moderately difficult prompts
Evaluate the performance of DARE against existing methods
Apply DARE to large language models to improve reasoning ability
Analyze the limitations of existing difficulty-aware data selection methods

Who Needs to Know This

ML researchers and engineers working on reinforcement learning and large language models can benefit from this approach to improve sample efficiency and reduce costs

Key Insight

💡 Co-evolving difficulty estimation with policy learning can improve sample efficiency and reduce costs in reinforcement learning

Full Article

Title: DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation

Abstract:
arXiv:2605.09188v1 Announce Type: cross Abstract: Reinforcement learning improves the reasoning ability of large language models but remains costly and sample-inefficient, as many rollouts provide weak learning signals. Difficulty-aware data selection methods attempt to address this by prioritizing moderately difficult prompts, yet our analysis reveals three limitations: difficulty estimates become inaccurate under policy drift, data selection alone yields limited final-performance gains, and in

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