arXiv:2602.08499v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language models. However, existing RLVR methods utilize rollouts in an indiscriminate and short-horizon manner: responses of heterogeneous quality within each prompt are treated uniformly, and historical rollouts are discarded after a single use. This leads to noisy supervision, poor sample efficiency, and suboptimal policy updates. We address these issues by formulating rollout scheduling in RLVR as a contextual ba
This research addresses fundamental limitations in current Reinforcement Learning with Verifiable Rewards (RLVR) methods, which are becoming critical as LLMs are increasingly deployed in real-world applications.
Improving the efficiency and effectiveness of RLVR directly enhances the reasoning capabilities and reliability of large language models, impacting their practical utility across various sectors.
The proposed contextual bandit approach shifts RLVR from indiscriminate feedback to smarter, more sample-efficient supervision, leading to more robust and accurate AI policy updates.
- · AI developers
- · LLM-powered applications
- · Data scientists
- · Inefficient RLVR methods
- · Computational waste
More capable and trustworthy large language models emerge from improved training paradigms.
Advanced LLMs accelerate the development and deployment of sophisticated AI agents.
The enhanced reasoning capabilities of AI agents begin to streamline and automate complex white-collar workflows, potentially displacing routine cognitive tasks.
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Read at arXiv cs.LG