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PROS: Towards Compute-Efficient RLVR via Rollout Prefix Reuse

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
RLVRreasoning
Abstract:

Large reasoning models (LRMs) trained with Reinforcement Learning with Verifiable Rewards (RLVR) have achieved remarkable progress on complex reasoning tasks. However, RLVR heavily relies on on-policy rollout generation, whose cost grows rapidly with rollout length and model size, eventually becoming the training bottleneck. Our empirical analysis reveals that independent rollouts for the same query often share similar early steps, indicating substantial redundancy. To address this, we propose Pros (Prefix Reuse for On-policy Sampling), a paradigm that reuses promising prefixes of historical rollouts in RLVR training. Pros appends these self-generated partial rollouts to the original queries to form Augmented Queries, which are then used as regular training inputs in subsequent iterations, thereby reducing redundant computation. To select training batch from augmented queries, Pros adopts a hierarchical Bayesian model to estimate their pass rates and prioritize those with the highest reward uncertainty. Experiments across diverse settings show that Pros consistently improves training efficiency and achieves higher accuracy than strong baselines. These results highlight Pros as a practical path toward scalable and compute-efficient RLVR.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes PROS, a paradigm that reuses promising prefixes from historical rollouts to reduce computational costs in reinforcement learning with verifiable rewards. According to the taxonomy, this work resides in the 'Prefix-Based Query Augmentation with Hierarchical Selection' leaf, which currently contains no sibling papers. The broader parent category 'Rollout Redundancy Reduction via Prefix Reuse' includes only one other leaf ('Speculative Rollout Acceleration'), suggesting this is a relatively sparse research direction with limited prior exploration of prefix-based augmentation combined with hierarchical selection mechanisms.

The taxonomy reveals two main branches: rollout redundancy reduction and adaptive sampling optimization. PROS sits in the first branch, which emphasizes identifying and sharing common trajectory prefixes. The neighboring 'Speculative Rollout Acceleration' leaf focuses on exploiting redundancy through speculative execution rather than query augmentation. The second branch ('Adaptive Sampling and Response Reuse Optimization') addresses vanishing advantages through response-level reuse, distinct from PROS's prefix-level approach. This positioning suggests PROS occupies a niche between speculative methods and adaptive response strategies, combining prefix reuse with hierarchical selection in a way that differs from existing acceleration techniques.

Among five candidates examined, two appear to provide overlapping prior work for the core PROS paradigm contribution. The hierarchical Bayesian model for augmented query selection and the promising prefix identification mechanism were not examined against any candidates, indicating these components received no direct prior work analysis within this limited search scope. The statistics suggest that while the overarching prefix reuse concept has some precedent among the examined papers, the specific combination of query augmentation and hierarchical selection may represent a less-explored integration. However, this assessment is constrained by the small candidate pool of five papers.

Based on the limited search scope of five candidates, PROS appears to introduce a specific combination of techniques—prefix-based query augmentation with hierarchical Bayesian selection—that occupies a sparse position in the taxonomy. The analysis does not cover exhaustive literature review or broader semantic search beyond top-K matches, leaving open the possibility of additional relevant work in adjacent areas such as trajectory caching, curriculum learning with partial rollouts, or other RL efficiency methods not captured in this focused examination.

Taxonomy

Core-task Taxonomy Papers
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Claimed Contributions
5
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Compute-efficient reinforcement learning with verifiable rewards via rollout prefix reuse. The field addresses the computational burden of generating numerous rollouts during RL training by exploiting redundancy in sampled trajectories. The taxonomy reveals two main branches: one focused on reducing rollout redundancy through prefix reuse mechanisms, and another centered on adaptive sampling and response reuse optimization. The first branch emphasizes techniques that identify and share common trajectory prefixes across multiple queries or policy updates, thereby avoiding redundant computation when generating similar rollouts. The second branch explores dynamic strategies for deciding when and how to reuse previously generated responses, balancing exploration with computational savings. Together, these branches reflect a growing recognition that many RL scenarios—especially those with verifiable rewards—can benefit from intelligent caching and reuse of partial rollouts. Recent work has highlighted contrasting trade-offs between aggressive prefix sharing and more conservative adaptive reuse. PROS[0] sits within the prefix-based query augmentation cluster, proposing hierarchical selection methods to maximize reuse while maintaining diversity in the training signal. This approach contrasts with methods like SPEC-RL[1], which may prioritize speculative execution or verification-driven sampling, and Adaptive Rollout Reuse[2], which dynamically adjusts reuse policies based on observed reward signals. A central open question across these lines is how to balance the computational savings from reuse against the risk of introducing bias or reducing exploration. PROS[0] addresses this by combining prefix reuse with hierarchical selection, aiming to preserve sample quality while cutting redundant generation costs, positioning it as a middle ground between fully speculative and purely adaptive strategies.

Claimed Contributions

PROS paradigm for compute-efficient RLVR via prefix reuse

The authors introduce PROS (Prefix Reuse for On-policy Sampling), a training paradigm that constructs Augmented Queries by appending promising prefixes from historical rollouts to original queries. This approach reduces redundant computation in RLVR training by reusing self-generated partial solutions instead of regenerating similar early reasoning steps.

5 retrieved papers
Can Refute
Hierarchical Bayesian model for augmented query selection

The authors develop a two-layer hierarchical Bayesian model that estimates pass rates of augmented queries from historical reward observations. This model prioritizes queries with pass rates near 0.5 (highest reward uncertainty) for training, leveraging the tree-structured relationship between original queries and their derived augmented queries.

0 retrieved papers
Promising prefix identification using entropy and value signals

The authors propose a method to identify high-quality rollout prefixes for reuse by combining token-level entropy (as an uncertainty signal) with value function predictions from critic models. This selection mechanism identifies prefixes that lie in informative regions of the reasoning space while maintaining computational efficiency through length constraints.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

PROS paradigm for compute-efficient RLVR via prefix reuse

The authors introduce PROS (Prefix Reuse for On-policy Sampling), a training paradigm that constructs Augmented Queries by appending promising prefixes from historical rollouts to original queries. This approach reduces redundant computation in RLVR training by reusing self-generated partial solutions instead of regenerating similar early reasoning steps.

Contribution

Hierarchical Bayesian model for augmented query selection

The authors develop a two-layer hierarchical Bayesian model that estimates pass rates of augmented queries from historical reward observations. This model prioritizes queries with pass rates near 0.5 (highest reward uncertainty) for training, leveraging the tree-structured relationship between original queries and their derived augmented queries.

Contribution

Promising prefix identification using entropy and value signals

The authors propose a method to identify high-quality rollout prefixes for reuse by combining token-level entropy (as an uncertainty signal) with value function predictions from critic models. This selection mechanism identifies prefixes that lie in informative regions of the reasoning space while maintaining computational efficiency through length constraints.

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