DE-Venus: A Data-Efficient RLVR Framework for Large Language Models
Shenzhi Yang, Guangcheng Zhu, Kai Tang, Zhengqing Zang, Xing Zheng, Haobo Wang, Yingfan Ma, Bowen Song, et al.
cs.LG
Quick verdict
Unified verl-based framework for data-efficient RLVR; well-engineered with real savings, but small margins lack variance checks and key methods are self-cited.
Summary
DE-Venus is a framework built on the verl distributed RL runtime that unifies data-efficient reinforcement learning with verifiable rewards (RLVR) by treating supervision as evolving state. It organizes interventions into three modules — Active Data Selection (offline generate-score-route pipelines), Weak Supervision Construction (pseudo-target and target-free online trainers such as TTRL, Co-rewarding, Intuitor, EM-RL), and Training-Time Supervision Refinement (TraPO, GeoMin, online label refinement) — implemented through verl-native contracts (target substitution, token-level rewards, aligned DataProto selections, advantage transforms) rather than a forked backend. On DeepMath-103K with Qwen3-8B-Base, GeoMin with 10% of labels reaches 47.9% ID and 69.5% OOD versus 46.7%/69.0% fully supervised; on DAPO-Math-14K with Qwen3-4B-Base, PivotTrace selection using 57.9% of data and 29.3% of annotations slightly exceeds full supervision. OLR improves accuracy at every tested noise ratio (ρ=0.1–0.9, inactive and active regimes) on Qwen3-4B-Base. Three anonymized business scenarios report up to +14 normalized points, 28% data removal within 1.9 points of full-data training, and 63–75% fewer observed convergence steps. Main limitations: single-run results without seeds, confidence intervals, or significance tests; margins as small as +0.3/+0.6 points; the winning methods are largely the authors' own preprints; business scenarios use internal data with suppressed absolute scores; and the rollout-compute overhead of weak supervision is not quantified. Code is stated to be available on GitHub.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Supervision in data-efficient RLVR can be unified as evolving state handled at three intervention points while preserving verl's distributed execution contracts.
- On DeepMath-103K with Qwen3-8B-Base, GeoMin using only 10% of labels reaches 47.9% ID / 69.5% OOD versus 46.7%/69.0% under full supervision.
- OLR improves ID and OOD accuracy at every tested noise ratio (ρ=0.1–0.9) in both inactive and active noise regimes on Qwen3-4B-Base.
- PivotTrace-based data selection using 57.9% of training data and 29.3% of annotations slightly exceeds fully supervised full-data training on DAPO-Math-14K.
- In three anonymized business deployments, DE-Venus yields up to +14 normalized points, 28% data removal within 1.9 points of full data, and 63–75% fewer observed convergence steps.
Strengths
Contract-preserving systems design
Interventions localize to four verl-native surfaces — target substitution, token-level rewards, aligned DataProto selections, advantage transforms (Table 2, Eq. 3) — plus offline Parquet transitions with manifests (Eq. 4); the state-ownership rules of §2.7 keep cross-round changes auditable, a genuine reproducibility improvement over trainer forks.
Matched controlled protocol
All comparisons hold GRPO, model, dataset loader, rollout engine, resource allocation, and checkpoint path fixed (§2.1, §4 opening); one benchmark suite spans ID math (AIME 2024/2025, AMC, MATH-500, Minerva, OlympiadBench) and OOD (ARC-c, GPQA-Diamond, MMLU-Pro) with stated decoding settings.
Negative controls in business validation
Informative noise design
Supervision observability
Weaknesses
No variance or significance reporting
Self-preferential method roster
Unverifiable business evidence
Internal inconsistencies
Efficiency framing ignores compute overhead
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Scope & Claims
DE-Venus (arXiv 2609.03324) is a systems/framework paper from Zhejiang University and Ant Group. It frames data-efficient RLVR as a supervision lifecycle with three intervention modules — Active Data Selection, Weak Supervision Construction, and Training-Time Supervision Refinement — implemented as a control layer over the verl distributed RL runtime (§2). It supports seven methods (TTRL, Tok/Seq-entropy, Self-certainty, Co-rewarding, TraPO, GeoMin), an active-selection pipeline (Random through PivotTrace), and online label refinement (OLR). Central claims: (1) method-specific supervision logic localizes to verl-native contracts — target substitution (reward_model.ground_truth), token-level rewards, aligned DataProto selections, advantage transforms (Table 2, Eq. 3); (2) 10% of labels can match or beat full supervision (GeoMin 47.9% ID / 69.5% OOD vs 46.7%/69.0%, §4.1.2); (3) OLR helps at every noise ratio ρ=0.1–0.9 (§4.2.2); (4) PivotTrace selection at 57.9% data / 29.3% annotations slightly exceeds full supervision (§4.3.2); (5) business deployments realize +14 normalized points, 28% data removal within 1.9 points, and 63–75% fewer convergence steps (§4.4).
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
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Frequently asked questions
What is DE-Venus and how does it differ from verl?
verl is an open-source distributed reinforcement-learning training runtime for LLMs. DE-Venus adds a supervision control layer on top of it: data selection before training, weak-supervision construction during training (majority-vote pseudo targets, entropy or self-certainty rewards), and label filtering or correction, while keeping verl's distributed execution, rollout, and optimization unchanged.
Can RLVR really match full supervision with only 10% of the labels?
In this paper's §4.1 (Qwen3-8B-Base on the DeepMath-103K difficulty-6+ subset), the semi-supervised GeoMin method reached 47.9% ID / 69.5% OOD average accuracy versus 46.7%/69.0% for fully supervised training. This is a single-run result on one model-dataset pair, so it should be read as promising rather than settled.
Is the DE-Venus code public?
The paper states the code is available at github.com/ZJU-DIG/DE-Venus (footnote 1). It does not document the license or release completeness, so verify the repository before relying on it.
Which benchmarks does the paper evaluate on?
In-domain math: AIME 2024, AIME 2025, AMC, MATH-500, Minerva, and OlympiadBench. Out-of-domain: ARC-c, GPQA-Diamond, and MMLU-Pro. Three additional anonymized internal business scenarios (loan-credit assignment, medical-empathy training, intrinsic-safety training) are also reported with normalized scores.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 3 and 5 appear only as captions without per-bar numeric values in the extraction; only text-quoted aggregates (e.g., 47.9/69.5 in §4.1.2, 48.9/64.0 in §4.3.2) could be verified. The provided text ends with the reference list and appears complete; no truncation detected.
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