RL2ML: Finite-Rollout Surrogate Objectives from Reinforcement Learning to Maximum Likelihood
arXiv:2605.30154v1 Announce Type: new Abstract: Correctness-based Reinforcement Learning with Verifiable Rewards (RLVR) trains language models from binary feedback on sampled outputs, but the objective optimized in expectation and the stochastic update geometry induced by finite rollout groups are often conflated. This paper develops RL2ML, a family of finite-rollout surrogate objectives with a closed-form, exactly unbiased gradient estimator. The family continuously connects standard reinforcement learning, maximum-likelihood-like training, and beyond-maximum-likelihood objectives while prese
The continuous evolution of AI language models demands more efficient and robust training methodologies, leading researchers to refine reinforcement learning techniques.
Improved training objectives like RL2ML can lead to more stable, unbiased, and capable AI models, accelerating their development and deployment across various applications.
The proposed RL2ML framework offers a more refined approach to optimizing AI models based on binary feedback, potentially reducing training instability and improving performance.
- · AI researchers
- · Language model developers
- · Companies deploying AI agents
- · Developers relying solely on older RL methods
More efficient and effective training of large language models for various tasks.
Accelerated development of sophisticated AI agents with more reliable decision-making capabilities.
Enhanced AI system robustness could reduce instances of AI misuse due to misaligned objectives or faulty training.
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