A-RSI：自适应任务刷新实现零数据多模态自改进
A-RSI: Adaptive Task Refresh for Zero-Data Multimodal Self-Improvement
A-RSI 通过基于学习信号下降刷新训练任务，并使用可独立验证的程序生成答案，在无需外部标签的情况下提升基准测试性能。该模型监控训练日志中的奖励变化和准确率来刷新任务池，并利用静态模型自编程库生成多模态问题及其参考答案，同时自动验证奖励和刷新规则的属性并审计生成答案。在十一项基准测试中，A-RSI 通过 800 个新任务将平均性能从 57.77 提升至 61.05。
Published on Oct 28
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Abstract
A-RSI improves recursive self-improvement by refreshing training tasks based on declining learning signals and using independently verifiable program-generated answers, raising benchmark performance without external labels.
Recursive self-improvement requires models not only to acquire useful training experience, but also to decide when their current experience has ceased to be useful. Existing methods often focus on what experience to generate next, while paying less attention to when fresh experience should be introduced. Many also construct training targets through self-consistency or majority voting, making supervision depend on the model's own uncertain predictions. We address these two questions separately: training dynamics determine when to acquire new experience, while the answers to new tasks remain independently checkable. A-RSI monitors reward variation and accuracy in training logs to refresh a task pool when its learning signal declines, without consulting validation or test scores. A static library of model-authored programs produces multimodal questions and derives their reference answers; we specify the task-generation contract, automatically verify chosen properties of the reward and refresh rules, and separately audit the generated answers. The training loop uses no external training examples or human-written answer labels. On the primary eleven-benchmark suite, A-RSI raises the performance average from 57.77 to 61.05 with 800 fresh tasks.
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来源: Hugging Face Daily Papers · huggingface.co