arXiv:2605.28247v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a key technique for en- hancing LLM reasoning, yet its data ineffi- ciency remains a major bottleneck. Existing methods address this problem only partially, each missing at least one of subset-level cov- erage, verifier signal use, or interpretability. To address this gap, we present IRDS (Inter- pretable RLVR Data Selection), which selects RLVR training instances on a sparse autoen- coder (SAE) cluster basis so the selection itself is auditable on recognizable problem motifs. To se
The increasing sophistication and scale of LLMs highlight data inefficiency as a critical bottleneck, making solutions like IRDS timely for advancing verifiable and robust AI systems.
This research directly addresses a core limitation in LLM development by improving data efficiency and interpretability in reinforcement learning with verifiable rewards, crucial for responsible AI deployment.
The ability to select more efficient and auditable training data for RLVR could accelerate LLM development, reduce computational costs, and enhance the trustworthiness of AI decisions by making selection criteria understandable.
- · AI developers
- · LLM researchers
- · Cloud infrastructure providers
- · SaaS companies leveraging AI
- · Companies with inefficient data pipelines
- · AI models requiring massive, untargeted datasets
More efficient and interpretable RLVR training processes for LLMs.
Faster development and deployment of more reliable and auditable AI agents.
Reduced compute resource demand per unit of AI capability, potentially shifting competitive advantages.
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Read at arXiv cs.LG