SOURCE-LINKED INTELLIGENCE
Unlocking the Unsolvable: Teacher-Guided Curriculum for Data-Efficient RLVR
Reinforcement Learning with Verifiable Rewards (RLVR) has shown remarkable success in improving the mathematical reasoning of large language models. Yet problems beyond the model's current capability, where rollouts uniformly fail and no learning signal is produced, are structurally wasted despite marking the most informative training frontier. We show that these otherwise-inert problems can be unlocked via teacher-guided curriculum learning: partial reasoning traces from a stronger model create a graded difficulty landscape, and a backward-chaining curriculum progressively withdraws guidance
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T15:22:22.000Z
First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.