Discounted Beta-Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
arXiv:2603.18444v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for improving the reasoning capabilities of large language models. However, existing group-based RLVR methods often suffer from severe sample inefficiency. This inefficiency stems from reliance on point estimation of rewards from a small number of rollouts, leading to high estimation variance, variance collapse, and ineffective utilization of generated responses. In this work, we reformulate RLVR from a statistical estimation perspective b
The rapid expansion of large language models and their increasing deployment in complex tasks necessitate more efficient and reliable training paradigms to overcome current limitations.
Improving sample efficiency and verifiability in RL for LLMs can significantly accelerate AI development, reduce computational costs, and enhance the trustworthiness of AI systems.
The proposed method offers a statistical approach to reward estimation, potentially resolving current issues of sample inefficiency and variance in Reinforcement Learning with Verifiable Rewards (RLVR).
- · AI developers
- · Large language model companies
- · Reinforcement learning researchers
- · AI-driven automation sectors
- · Companies with inefficient RL training pipelines
- · AI products relying on high-variance reward models
More sophisticated and reliable large language models can be trained with less data and computational resources.
Accelerated deployment of autonomous AI agents capable of higher reasoning and verifiable outcomes.
Enhanced trust in AI decision-making, potentially leading to broader adoption in critical applications.
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Read at arXiv cs.LG