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Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
DE-Venus is a preprint presenting a unified software framework designed to reduce annotation and training costs in reinforcement learning with verifiable rewards for large language models. The authors report that selected configurations achieved model quality preservation or improvement using only 10% of labels or 13% of relevant data, and reduced convergence steps by 63–75% in some business scenarios, but these results are unreviewed, use proprietary test cases, and lack independent validation.
Framework development and empirical evaluation on public benchmarks and proprietary scenarios. Large language models; public benchmarks and three proprietary business use cases; no human subjects enrolled.. Intervention: DE-Venus framework: three-module system for data-efficient RLVR combining active data selection, weak supervision construction, and training-time supervision refinement.. Compared with: Implicit comparison to baseline (full-label, full-data) conditions; no named alternative methods compared in the source text..
DE-Venus framework organizes data-efficient RLVR into three modules: Active Data Selection, Weak Supervision Construction, and Training-Time Supervision Refinement. Across public benchmarks and three business scenarios, separate configurations preserved or improved model quality with only 10% of labels or as little as 13% of relevant data. Selected business configurations reduced observed convergence steps by 63–75%.
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This is a preprint describing a software framework and engineering approach with empirical results on benchmarks and proprietary business scenarios, but without peer review, clinical outcomes, or independent validation of the claimed efficiency gains.
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Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-policy rollouts and the cost of obtaining reliable targets at scale. Existing methods address sample selection, incomplete supervision, or noisy labels separately, often entangling supervision logic with distributed training and hindering controlled comparison and reuse. We present DE-Venus, a unified framework for data-efficient RLVR that treats supervision as evolving state across data preparation and policy optimization. It organizes this lifecycle into three modules: Active Data Selection allocates training and annotation budgets; Weak Supervision Construction derives learning signals from unlabeled examples; and Training-Time Supervision Refinement filters or corrects unreliable supervision. DE-Venus supports seven representative methods and a data-selection pipeline by expressing method-specific decisions as offline dataset transitions or online transformations of targets, rewards, batches, and advantages while preserving verl's distributed execution contracts. Across public benchmarks and three business scenarios, separate configurations preserve or improve model quality with only 10% of labels or as little as 13% of relevant data; selected business configurations also reduce observed convergence steps by 63%--75%. DE-Venus thus reduces annotation and training costs without sacrificing scalable RL execution.
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