Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots
arXiv:2606.04503v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset. To this end, data-efficient RLVR methods have been widely studied from two perspectives: (i) data selection methods identify a small subset of "golden" samples that yield near-full-data performance, but they rely on a pre-existing pool of labeled data. (ii) unsupervised RLVR methods train the model using its own internal supervision signals on large-scale unlabeled data, yet t
The continuous drive for more efficient and data-sparing methods in AI model training is critical as models scale and data annotation becomes increasingly costly. This method addresses core limitations in current RLVR approaches for large reasoning models.
Improving data efficiency in reinforcement learning for large reasoning models accelerates AI development, reduces computational and data costs, and broadens the applicability of advanced AI systems beyond resource-rich entities.
This research outlines a pathway to significantly reduce the data and annotation requirements for training large reasoning models, potentially democratizing access to powerful AI capabilities and lowering barriers to entry in AI development.
- · AI researchers and developers
- · Companies with limited annotated datasets
- · Startups in AI development
- · Providers of extensive data annotation services (marginally)
More advanced and capable AI models can be trained with less specialized data.
This could lead to a faster pace of innovation in AI-driven products and services due to reduced development overheads.
Reduced data dependency might decentralize AI development, fostering a more diverse and competitive AI ecosystem globally.
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Read at arXiv cs.LG