模型测试时自动选题训练，持续提升解题能力。
Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning
- 测试时动态构建针对性训练题库，替代人工设计数据集。
- 在数学与编程基准上，性能提升1.8至2.1倍，最高达62%通过率。
- 适合需要持续优化的复杂任务场景，如AI解题系统升级。
人类擅长边工作边学习：在面对任务时同步掌握解决方法。能否让模型也做到？我们提出一种智能体，可自动生成任务专属的测试时课程（TTC-RL），并基于强化学习对目标任务持续训练。该方法通过从大规模训练数据中自动筛选最相关的样本，避免耗时的人工数据标注。实验表明，使用测试时课程的强化学习能稳定提升模型在多种任务上的表现。尤其在挑战性数学和编码基准上，TTC-RL使Qwen3-8B的pass@1分别提升约1.8倍（AIME25）和2.1倍（CodeElo）。此外，其显著提升了性能上限：AIME25的pass@8从40%升至62%，CodeElo从28%升至43%。结果表明，测试时课程有望将测试时扩展范式推广至千级任务相关经验的持续训练。
原文摘要 · Abstract (English)
Humans are good at learning on the job: We learn how to solve the tasks we face as we go along. Can a model do the same? We propose an agent that assembles a task-specific curriculum, called test-time curriculum (TTC-RL), and applies reinforcement learning to continue training the model for its target task. The test-time curriculum avoids time-consuming human curation of datasets by automatically selecting the most task-relevant data from a large pool of available training data. Our experiments demonstrate that reinforcement learning on a test-time curriculum consistently improves the model on its target tasks, across a variety of evaluations and models. Notably, on challenging math and coding benchmarks, TTC-RL improves the pass@1 of Qwen3-8B by approximately 1.8x on AIME25 and 2.1x on CodeElo. Moreover, we find that TTC-RL significantly raises the performance ceiling compared to the initial model, increasing pass@8 on AIME25 from 40% to 62% and on CodeElo from 28% to 43%. Our findings show the potential of test-time curricula in extending the test-time scaling paradigm to continual training on thousands of task-relevant experiences during test-time.
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