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[2602.14868] Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning
Summary
The paper introduces Goldilocks RL, a novel approach in reinforcement learning that adjusts task difficulty to enhance reasoning capabilities in models, addressing the challenge of sparse rewards.
Why It Matters
This research is significant as it tackles the inefficiencies in reinforcement learning due to sparse rewards, proposing a dynamic method for task difficulty adjustment. By improving model training through a teacher-student framework, it has implications for advancing AI reasoning, particularly in complex problem-solving scenarios.
Key Takeaways
- Goldilocks RL uses a teacher-driven strategy to optimize task difficulty.
- The approach enhances model performance on reasoning tasks by adapting to student abilities.
- It addresses inefficiencies in reinforcement learning caused by sparse rewards.
- The method shows improved results on the OpenMathReasoning dataset.
- Dynamic difficulty adjustment can lead to more effective AI training.
Computer Science > Machine Learning arXiv:2602.14868 (cs) [Submitted on 16 Feb 2026] Title:Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning Authors:Ilia Mahrooghi, Aryo Lotfi, Emmanuel Abbe View a PDF of the paper titled Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning, by Ilia Mahrooghi and 2 other authors View PDF HTML (experimental) Abstract:Reinforcement learning has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models. However, relying on sparse rewards makes this process highly sample-inefficient, as models must navigate vast search spaces with minimal feedback. While classic curriculum learning aims to mitigate this by ordering data based on complexity, the right ordering for a specific model is often unclear. To address this, we propose Goldilocks, a novel teacher-driven data sampling strategy that aims to predict each question's difficulty for the student model. The teacher model selects questions of appropriate difficulty for the student model, i.e., questions that are neither too easy nor too hard (Goldilocks principle), while training the student with GRPO. By leveraging the student's performance on seen samples, the teacher continuously adapts to the student's evolving abilities. On OpenMathReasoning dataset, Goldilocks data sampling improves the performance of models trained with standard GRPO under the same compute budget. Comments: Subjects: Machine Learning (cs....