yukangzhu/unlocking-the-unsolvable

Tasks

• text-generation

Citation text from the card

@inproceedings{zhu-han-2026-unlocking,
    title = "Unlocking the Unsolvable: Teacher-Guided Curriculum for Data-Efficient {RLVR}",
    author = "Zhu, Yukang  and
      Han, Zhen",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.931/",
    pages = "19639--19658",
    ISBN = "979-8-89176-462-0",
    abstract = "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 until the model solves problems unaided. Training on only 128 unsolvable problems matches or exceeds GRPO trained on a full 2,000-problem corpus ({\textasciitilde}16{\texttimes} data efficiency) on the nine-benchmark average for both base models, w

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